This Advanced Seminar provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) for Urban Mobility applications. The presented work in this seminar is categorized based on how LLMs are employed to serve various urban mobility applications. This goes from employing LLMs as a black box with a bit of prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to completely retrain a vanilla LLM architecture with urban mobility data, to modifying the internal LLM loss function to fit urban mobility applications. The seminar concludes by presenting a set of benchmarking and evaluation work while pointing out to research gaps, open problems, and future research directions for employing LLMs to urban mobility applications.

The evolution of data-driven algorithms for trajectory analysis operations relies heavily on the availability of trajectory data. Unfortunately, most of the available trajectory datasets are not suitable for use by analysis operations. A main reason is that such trajectories are released in their raw form: A sequence of locations coming from the same device over a time period (e.g.: hours or years), whereas trajectory analysis operations need trip trajectories. Hence, existing trajectory analysis techniques preprocess the raw trajectories by applying simple rules to extract trips out of each trajectory. However, such basic rules miss too many realistic scenarios and result in low accuracy which negatively affects down-stream trajectory applications. This paper presents TrajSplit: an accurate and scalable algorithm for trip extraction from raw GPS trajectories. TrajSplit goes beyond the basic simple rules to introduce a realistic definition of a trip, which can be realized through a computationally expensive brute force approach. Therefore, TrajSplit offers two scalable heuristic approaches, that still achieve a very similar accuracy to its brute force. Experimental results, based on two real datasets, show that TrajSplit: (a) is far more accurate than the basic rules, and (b) is highly scalable when employing either of the heuristics.

Though polar scientists entertain having huge amounts of publicly available datasets, they face the challenge that working with such data is a cumbersome process that requires downloading tons of unnecessary data and writing various scripts on top of it. This hinders their ability to perform any kind of interactive analysis. This paper presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Experimental results with a real Polaris prototype and real scientific data show that it achieves highly interactive and scalable data access, enabling interactive analysis of polar science data.

This demonstration presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. With this, Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Audience will be able to experience Polaris through various scenarios that show the interactivity and scalability as well as Polaris optimized query processes.

This tutorial provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) to spatial analysis queries. The tutorial categorizes the research in this area based on how LLMs are employed to serve such queries. This goes from employing LLMs as is, to fine-tuning LLMs, to completely retrain LLM architectures, to modifying the LLM internals to fit spatial queries. The tutorial concludes by a set of benchmarks and pointing out to research gaps and future research directions.

With the surge of data-driven solutions for trajectory analysis operations, the need for accurate trajectory trip data has spiked. However, the available datasets are raw trajectories spanning from hours to years, not representing actual trips for downstream applications. Therefore, pre-processing steps, such as basic rules to extract trips, are needed to use the datasets. However, this paper demonstrates that the current pre-processing steps are not enough and result in low accuracy, negatively affecting the downstream applications. This paper presents an overview of an accurate and scalable algorithm for splitting raw trajectories for trip extraction. We go beyond the basic rules to introduce a realistic definition of a trip and offer two scalable heuristics over the exhaustive brute force approach of the algorithm with similar accuracy. Experimental results show that the proposed algorithm is: (a) far more accurate than the basic rules, (b) scalable when employing either of the heuristics.

In this paper, we present an innovative framework whose objective is to allow drivers to recharge their Electric Vehicles (EVs) from the most environmentally friendly chargers using an intelligent hoarding approach. These chargers maximize renewable (e.g., solar) self-consumption, minimizing this way CO2 production and also the need for expensive stationary batteries on the electricity grid to store renewable energy that cannot be used otherwise. We model our problem as a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs), i.e., a query we term CkNN-EC. An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. We devise the EcoCharge framework that combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive mobile GIS application. Particularly, our core algorithm uses lower and upper values derived from the ECs to recommend the top ranked EV chargers and present them through an intuitive map user interface to users. Our experimental evaluation with extensive synthetic and real traces from Germany, China, and USA along with EV charger data from Plugshare shows that EcoCharge meets the objective functions in an efficient manner, allowing continuous recomputation on the edge devices (e.g., Android Automotive OS, Android Auto or Apple Carplay).

In this demonstration paper, we present an innovative framework for sustainable Electric Vehicles (EVs) charging, dubbed EcoCharge, which utilizes an intelligent energy hoarding approach. Particularly, EcoCharge employs a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs) (i.e., a query we term CkNN-EC). An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. Our framework combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive spatial application. The algorithm utilizes lower and upper interval values derived from ECs to recommend the top ranked EV chargers and present them through a map interface to users. We demonstrate EcoCharge using a complete prototype system developed using the Leaflet - OpenStreetMap library. In our demonstration scenario, attendees will have the opportunity to observe through mobile devices the benefits of EcoCharge by simulating its execution over various scheduled trips with real data retrieved from API requests (i.e., ECs).

Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to learn that a certain value of an attribute leads to a corresponding value of another attribute. However, for spatial attributes that represent locations, there is very little chance that two records would have the same exact coordinates, and hence co-occurrence is unlikely to exist. This paper presents Sparcle (SPatially-AwaRe CLEaning); a novel framework that injects spatial awareness into the core engine of rule-based data cleaning systems through two main concepts: (1) Spatial Neighborhood , where co-occurrence is relaxed to be within a certain spatial proximity rather than same exact value, and (2) Distance Weighting , where records are given different weights of whether they satisfy a dependency rule, based on their relative distance. Experimental results using a real deployment of Sparcle inside a state-of-the-art data cleaning system, and real and synthetic datasets, show that Sparcle significantly boosts the accuracy of data cleaning systems when dealing with spatial data.

GPS-enabled devices, including vehicles, smartphones, wearable and tracking devices, as well as various check-in and social network data are continuously producing tremendous amounts of trajectory data, which are used consistently in many applications such as urban planning and map inference. Existing techniques for trajectory data imputation rely heavily on the existing maps to perform map-matching operations. However, modern applications such as map construction and map update assume no map exists. In this paper, we propose GTI - a scalable graph-based trajectory imputation approach for trajectory data completion. GTI relies on cross-trajectory imputation, as it exploits "mutual information" of the aggregated knowledge of all input sparse trajectories to impute the missing data for each single one of them. GTI can act as a pre-processing step for any trajectory data management system or trajectory-based application, as it takes raw sparse trajectory data as its input and outputs dense imputed trajectory data that significantly increase the accuracy of different systems that consume trajectory data. We evaluate GTI on junction-scale as well as city-scale real datasets. In addition, GTI is used as a pre-processing step in multiple trajectory-based applications and it boosts the accuracy across these applications compared with the state-of-the-art work.

This paper envisions having an open-source web portal for detailed worldwide road network maps with rich metadata. This would be major advancement from current portals that only have road networks without important metadata, including traffic-related ones. The envisioned portal will not only enable researchers to exploit more practical research, but would also enable practitioners and small/medium enterprises to avoid the high cost of commercial maps. The paper presents eight directions that can be exploited towards realizing the vision and acts as an invitation to the community to exploit these directions.

Numerous important applications rely on detailed trajectory data. Yet, unfortunately, trajectory datasets are typically sparse with large spatial and temporal gaps between each two points, which is a major hurdle for their accuracy. This paper presents Kamel; a scalable trajectory imputation system that inserts additional realistic trajectory points, boosting the accuracy of trajectory applications. Kamel maps the trajectory imputation problem to finding the missing word problem; a classical problem in the natural language processing (NLP) community. This allows employing the widely used BERT model for trajectory imputation. However, BERT, as is, does not lend itself to the special characteristics of trajectories. Hence, Kamel starts from BERT, but then adds spatial-awareness to its operations, adjusts trajectory data to be closer to the nature of language data, and adds multipoint imputation ability to it; all encapsulated in one system. Experimental results based on real datasets show that Kamel significantly outperforms its competitors and is applicable to city-scale trajectories, large gaps, and tight accuracy thresholds.

The ability to collect large numbers of trajectory data through GPS-enabled devices have enabled a myriad of very important applications that are widely used on a daily basis. This includes urban computing, transportation, and map APIs for routing and navigation. Unfortunately, a major hinder for all these applications is the accuracy of collected trajectories. Due to low sampling rates, trajectories are usually sparse in terms of the large spatial and temporal distances between each two consecutive collected points. This paper presents TrImpute; a novel framework for trajectory imputation that inserts artificial GPS points between the real ones in a way that the imputed trajectories end up to be very similar to the case if such trajectories were collected with a much higher sampling rate. Unlike all prior trajectory imputation techniques, TrImpute does not assume the knowledge of the underlying road network. This makes it more practical when the underlying road network is not available or inaccurate. Experimental results on real datasets and a real deployment of TrImpute show that it is highly scalable, accurate, and can significantly boost the performance of trajectory applications by feeding them highly accurate trajectories.

Trajectory-based applications have acquired significant attention over the past decade with the rising size of trajectory data generated by users. However, building trajectory-based applications is still cumbersome due to the lack of unified frameworks to tackle the underlying trajectory analysis challenges. Inspired by the tremendous success of the BERT deep learning model in solving various NLP tasks, our vision is to have a BERT-like system for a myriad of trajectory analysis operations. We envision that in a few years, we will have such system, where no one needs to worry again about each specific trajectory analysis operation. Whether it is trajectory imputation, similarity, clustering, or whatever, it would be one system that researchers, developers, and practitioners can deploy to get high accuracy for their trajectory operations.

Road network queries (e.g., shortest path, range, and k-NN) hinge on the road network quality, which, un-fortunately, suffer from all sorts of inaccuracy. Given that OpenStreetMap (OSM) has been the de facto open-source map for a myriad of widely used applications, this demo presents RASED; a publicly available scalable dashboard to interactively monitor and analyze billions of OSM updates worldwide. RASED provides the necessary infrastructure that is immensely needed by map analyzers to understand and assess the map quality for anywhere in the world, which is a measure of the query accuracy.

Recently ride-sharing platforms have struggled with a decreased supply of drivers, which has negatively impacted their passengers, by subjecting them to long delays and extremely high surge prices. An approach for mitigating these problems is for service providers to facilitate and coordinate carpooling via the recommendation of individually curated paths, not necessarily the shortest, for drivers towards completing their chosen rides. In this paper, we redesign the Weight Evolving Temporal graph structure to efficiently encode large dynamic road networks with temporal ride availability. Leveraging that graph structure, we efficiently define a polynomial-time optimal route recommendation algorithm that increases carpooling opportunities, taking into consideration the spatio-temporal constraints of both drivers and rides in such a highly-dynamic setting. Finally, we use simulations to demonstrate the effectiveness of these route recommendations, on both the driver and passenger experience.

Understanding the evolution and changes of digital road networks and how it resembles the true physical road network, have been a rich area of study within map analyzers, urban planners, and transportation communities. The main focus was to study OpenStreetMap (OSM) as the most commonly used platform for worldwide digital road networks, and is deemed even more accurate than commercial maps. However, all such studies have been localized to small areas of interest, mainly due to the large scale of the whole OSM road network. This paper presents RASED; a publicly available scalable dashboard to interactively monitor and analyze the evolution of all OSM road network. Using RASED, map analyzers can query and visualize various statistics about the road network daily changes worldwide, which would give a better understanding of the status of map quality and stability anywhere in the world. RASED relies on daily and monthly offline precomputations, accessed via a hierarchical temporal index structure. Experimental results show that RASED queries are always supported in the order of milliseconds, regardless of how large is the query temporal window, which allows highly interactive map analysis.

The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this 90-minutes seminar, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference. We also discuss the existing end-to-end systems, and highlight open problems and challenges for future research in this area.

This demo presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to: (a) construct its own highly accurate map in terms of both map topology and edge weights, and (b) calibrate its query answers based on contextual information, including transportation modality, underlying algorithm, and time of day/week. The demo is based on actual deployment of QARTA in all Taxis in the State of Qatar and in the third-largest food delivery company in the country, and receiving hundreds of thousands of daily API calls with a real-time response time. Audience will be able to interact with the demo through various scenarios that show QARTA map and query accuracy as well as internals of QARTA.

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.

This paper presents Sya; the first spatial probabilistic knowledge base construction system, based on Markov Logic Networks (MLN). Sya injects the awareness of spatial relationships inside the MLN grounding and inference phases, which are the pillars of the knowledge base construction process, and hence results in a better knowledge base output. In particular, Sya generates a probabilistic model that captures both logical and spatial correlations among knowledge base relations. Sya provides a simple spatial high-level language, a spatial variation of factor graph, a spatial rules-query translator, and a spatially-equipped statistical inference technique to infer the factual scores of relations. In addition, Sya provides an optimization that ensures scalable grounding and inference for large-scale knowledge bases. Experimental evidence, based on building two real knowledge bases with spatial nature, shows that Sya can achieve 70% higher F1-score on average over the state-of-the-art DeepDive system, while achieving at least 20% reduction in the execution times.

The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this 90-minutes tutorial, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference. We also discuss the existing end-to-end systems, and highlight open problems and challenges for future research in this area.

Travel time estimation is an important component in modern transportation applications. The state of the art techniques for travel time estimation use GPS traces to learn the weights of a road network, often modeled as a directed graph, then apply Dijkstra-like algorithms to find shortest paths. Travel time is then computed as the sum of edge weights on the returned path. In order to enable time-dependency, existing systems compute multiple weighted graphs corresponding to different time windows. These graphs are often optimized offline before they are deployed into production routing engines, causing a serious engineering overhead. In this paper, we present STAD, a system that adjusts - on the fly - travel time estimates for any trip request expressed in the form of origin, destination, and departure time. STAD uses machine learning and sparse trips data to learn the imperfections of any basic routing engine, before it turns it into a full-fledged time-dependent system capable of adjusting travel times to real traffic conditions in a city. STAD leverages the spatio-temporal properties of traffic by combining spatial features such as departing and destination geographic zones with temporal features such as departing time and day to significantly improve the travel time estimates of the basic routing engine. Experiments on real trip datasets from Doha, New York City, and Porto show a reduction in median absolute errors of 14% in the first two cities and 29% in the latter. We also show that STAD performs better than different commercial and research baselines in all three cities.

Driven by the ubiquity of location-based services, that produces a massive amount of moving objects. Querying and analyzing these data become a must for a wide range of applications. This demonstration presents a scalable data management framework. The proposed system is well-suited to efficiently support several basic queries, such as range, k NN, and similarity queries. These queries and the architectural design of the proposed system are extendable, in a way that enables users to build various applications and operations.

Commercial maps often offer traffic awareness which is critical for many location based services. On the other hand free and open map services (such as government maps or OSM) are traffic oblivious and hence are of limited value for such services. In this paper we show that coarse information available from a commercial map routing API, can be dissected into fine-grained per-road-segment traffic information which can be reused in any application requiring traffic-awareness. Our system MapReuse queries a commercial map for a (relatively small) number of routes, and uses the returned routes and expected travel times, to infer travel time on each individual edge of the road network. Such fine-grained travel time information can be used not only to infer travel time on any given route but also to compute complex spatial queries (such as traffic-aware isochrone map) for free. We test our system on four representative metropolitan areas: Bogota, Doha, NYC and Rome, and report very encouraging results. Namely, we observe the median and mean percentage errors of MapReuse, measured against the travel times reported by the commercial map, to be in the range of 4% to 8%, implying that MapReuse is capable to accurately reconstruct the traffic conditions in all four studied cities.

Event detection applications have gained significant attention with the rise of user-generated spatio-temporal data over the past decade. However, building event detection applications still encounter high cost and effort due to lack of support in existing data management systems. This paper envisions a holistic system approach to support an efficient and easy-to-use system infrastructure for building event detection applications. We outline our vision for representing event detection applications as a set of layered abstractions and discuss potential pathways to realize these abstractions at the system level.

The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this 90-minutes tutorial, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference, as well as two advanced spatial machine learning tasks, namely, spatial features extraction and spatial sampling. We also highlight open problems and challenges for future research in this area.

The current explosion in spatial data raises the need for efficient spatial analysis tools to extract useful information from such data. However, existing tools are neither generic nor scalable when dealing with big spatial data. This demo presents Flash ; a framework for generic and scalable spatial data analysis, with a special focus on spatial probabilistic graphical modelling (SPGM). Flash exploits Markov Logic Networks (MLN) to express SPGM as a set of declarative logical rules. In addition, it provides spatial variations of the scalable RDBMS-based learning and inference techniques of MLN to efficiently perform SPGM predictions. To show Flash effectiveness, we demonstrate three applications that use Flash in their SPGM: (1) Bird monitoring, (2) Safety analysis, and (3) Land use change tracking.

With the increase in amount of remote sensing data, there have been efforts to efficiently process it to help ecologists and geographers answer queries. However, they often need to process this data in combination with vector data, for example, city boundaries. Existing efforts require one dataset to be converted to the other representation, which is extremely inefficient for large datasets. In this demonstration, we focus on the zonal statistics problem, which computes the statistics over a raster layer for each polygon in a vector layer. We demonstrate three approaches, vector-based, raster-based, and raptor-based approaches. The latter is a recent effort of combining raster and vector data without a need of any conversion. This demo will allow users to run their own queries in any of the three methods and observe the differences in their performance depending on different raster and vector dataset sizes.

Geotagged data (e.g. images or news items) have empowered various important applications, e.g., search engines and news agencies. However, the lack of available geotagged data significantly reduces the impact of such applications. Meanwhile, existing geotagging approaches rely on the existence of prior knowledge, e.g., accurate training dataset for machine learning techniques. This paper presents Stella; a crowdsourcing framework for image geotagging. The high accuracy of Stella is resulted by being able to recruit workers near the image location even without knowing its location. In addition, Stella also return its confidence about the reported location to help users in understanding the result quality. Experimental evaluation shows that Stella consistently geotags an image with an average of 95% accuracy and 90% of confidence.

Predicting the presence or absence of spatial phenomena has been of great interest to scientists pursuing research in several applications including epidemic diseases detection, species occurrence prediction and earth observation. In this operation, a geographical space is divided by a two-dimensional grid, where the prediction (i.e, either 0 or 1) is performed at each cell in the grid. A common approach to solve this problem is to build spatial logistic regression models (a.k.a autologistic models) that estimate the prediction at any location based on a set of predictors (i.e., features) at this location and predictions from neighboring locations. Unfortunately, existing methods to build autologistic models are computationally expensive and do not scale up for large-scale grid data (e.g., fine-grained satellite images). This paper introduces TurboReg, a scalable framework to build autologistic models for predicting large-scale spatial phenomena. TurboReg considers both the accuracy and efficiency aspects when learning the regression model parameters. TurboReg is built on top of Markov Logic Network (MLN), a scalable statistical learning framework, where its internals and data structures are optimized to process spatial data. A set of experiments using large real and synthetic data show that TurboReg achieves at least three orders of magnitude performance gain over existing methods while preserving the model accuracy.

Understanding link travel times (LTT) has received significant attention in transportation and spatial computing literature but they often remain behind closed doors, primarily because the data used for capturing them is considered confidential. Consequently, free and open maps such as OpenStreetMap (OSM) or TIGER, while being remarkably accurate in capturing geometry and topology of the road network are oblivious to actual travel times. Without LTTs computing the optimal routes or estimated time of arrival is challenging and prone to substantial errors. In this work we set to enrich the underlying map information with LTT by using a most basic data about urban trajectories, which also becomes increasingly available for public use: set of origin/destination location/timestamp pairs. Our system, W-edge utilizes such basic trip information to calculate LTT to each individual road segment, effectively assigning a weight to individual edges of the underlying road network. We demonstrate that using appropriately trained edge weights, the errors in estimating travel times are up to 60% lower than the errors observed in OSRM or GraphHopper, two prominent OSM-based, traffic-oblivious, routing engines.

In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which does not exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup, we measure the efficiency of the proposed operator using a ~10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.

In this demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (TBD) into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.

This demo presents Sya; the first full-fledged spatial probabilistic knowledge base construction system. Sya is a comprehensive extension to the DeepDive system that enables exploiting the spatial relationships between extracted relations during the knowledge base construction process, and hence results in a better knowledge base output. Sya runs existing DeepDive programs as is, yet, it extracts more accurate relations than DeepDive when dealing with input data that have spatial attributes. Sya employs a simple spatial high-level language, a rule-based spatial SQL query engine, a spatially-indexed probabilistic graphical model, and an adapted spatial statistical inference technique to infer the factual scores of relations. We demonstrate a real system prototype of Sya, showing a case study of constructing a crime knowledge base. The demonstration shows to the audience the internal steps of building the knowledge base, as well as a comparison with the output of DeepDive.

International audience

This paper provides the first attempt for a full-fledged query optimizer for MapReduce-based spatial join algorithms. The optimizer develops its own taxonomy that covers almost all possible ways of doing a spatial join for any two input datasets. The optimizer comes in two flavors; cost-based and rule-based. Given two input data sets, the cost-based query optimizer evaluates the costs of all possible options in the developed taxonomy, and selects the one with the lowest cost. The rule-based query optimizer abstracts the developed cost models of the cost-based optimizer into a set of simple easy-to-check heuristic rules. Then, it applies its rules to select the lowest cost option. Both query optimizers are deployed and experimentally evaluated inside a widely used open-source MapReduce-based big spatial data system. Exhaustive experiments show that both query optimizers are always successful in taking the right decision for spatially joining any two datasets of up to 500GB each.

Motivated by its wide availability and richness, there have been a plethora of recent work in querying, analyzing, and visualizing microblogs (see [3] for a brief survey). Examples of microblogs include tweets, online reviews, and comments on news websites. Unfortunately, existing work in microblog lacks data management tools that provide the necessary infrastructure to support efficient storage, indexing, and retrieval of microblogs. Hence, researchers, developers, and practitioners who need to process microblogs for their own purposes would need to either build their own ad-hoc techniques [5] or use any of existing general purpose big data engines, e.g., Spark, as their backbone [4]. Relying on ad-hoc techniques does not scale for large data sizes. Meanwhile, existing general purpose big data engines are built in a generic way to support various query workloads. Thus, they are not equipped to support the characteristics of microblogs [2], and so they are missing necessary infrastructure like supporting the real-time indexing and promoting temporal, spatial, and ranking queries. This results in sub par performance when supporting microblogs. Video: http://kite.cs.umn.edu/video.html

In the realm of smart cities, telecommunication companies (telcos) are expected to play a protagonistic role as these can capture a variety of natural phenomena on an ongoing basis, e.g., traffic in a city, mobility patterns for emergency response or city planning. The key challenges for telcos in this era is to ingest in the most compact manner huge amounts of network logs, perform big data exploration and analytics on the generated data within a tolerable elapsed time. This paper introduces SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time, and (ii) minimizing the response time for spatiotemporal data exploration queries over recent data. The storage layer of our framework uses lossless data compression to ingest recent streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. The indexing layer of our system then takes care of the progressive loss of detail in information, coined decaying, as data ages with time. The exploration layer provides visual means to explore the generated spatio-temporal information space. We measure the efficiency of the proposed framework using a 5GB anonymized real telco network trace and a variety of telco-specific tasks, such as OLAP and OLTP querying, privacy-aware data sharing, multivariate statistics, clustering and regression. We show that out framework can achieve comparable response times to the state-of-the-art using an order of magnitude less storage space.

In this demonstration paper, we present SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time, and (ii) minimizing the response time for spatiotemporal data exploration queries over stored data. Our framework deploys lossless data compression to ingest streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. We augment our storage structures with decaying principles that lead to the progressive loss of detail as information gets older. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate SPATE in two modes: (i) Visual Mode, where attendees will be able to interactively explore synthetic telco traces we will provide, and (ii) SQL Mode, where attendees can submit custom SQL queries based on a provided schema.

Personalized recommendation has become popular in modern web services. For instance, Amazon recommends new items to shoppers. Also, Netflix recommends shows to viewers, and Facebook recommends friends to its users. Despite the ubiquity of recommendation applications, classic database management systems still do not provide in-house support for recommending data stored in the database. In this paper, we present the anatomy of RecDB an open source PostgreSQLbased system that provides a unified approach for declarative data recommendation inside the database engine. RecDB realizes the personalized recommendation functionality as query operators inside the database kernel. That facilitates applying the recommendation functionality and typical database operations (e.g., Selection, Join, Top-k) side-by-side. To further reduce the application latency, RecDB pre-computes and caches the generated recommendation in the database. In the paper, we present extensive experiments that study the performance of personalized recommendation applications based on an actual implementation inside PostgreSQL 9.2 using real Movie recommendation and location-aware recommendation scenarios. The results show that a recommendation-aware database engine, i.e., RecDB, outperforms the classic approach that implements the recommendation logic on-top of the database engine in various recommendation applications.

This demo presents Scout; a full-fledged interactive data visualization system with native support for spatio-temporal data. Scout utilizes computing power of GPUs to achieve real-time query performance. The key idea behind Scout is a GPU-aware multi-version spatio-temporal index. The indexing and query processing modules of Scout are designed to complement the GPU hardware characteristics. Front end of Scout provides a user interface to submit queries and view results. Scout supports a variety of spatio-temporal queriesrange, k-NN, and join. We use real data sets to demonstrate scalability and important features of Scout.

This demo presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop injects spatio-temporal awareness in the Hadoop base code, which results in achieving order(s) of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. The key idea behind ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System (HDFS). A real system prototype of ST-Hadoop, running on a local cluster of 24 machines, is demonstrated with two big-spatio-temporal datasets of Twitter and NYC Taxi data, each of around one billion records.

In this tutorial, we present the recent work in the database community for handling Big Spatial Data. This topic became very hot due to the recent explosion in the amount of spatial data generated by smart phones, satellites and medical devices, among others. This tutorial goes beyond the use of existing systems as-is (e.g., Hadoop, Spark or Impala), and digs deep into the core components of big systems (e.g., indexing and query processing) to describe how they are designed to handle big spatial data. During this 90-minute tutorial, we review the state-of-the-art work in the area of Big Spatial Data while classifying the existing research efforts according to the implementation approach, underlying architecture , and system components. In addition, we provide case studies of full-fledged systems and applications that handle Big Spatial Data which allows the audience to better comprehend the whole tutorial.

This paper demonstrates Stella ; an efficient crowdsourcing-based geotagging framework for any types of objects. In this demonstration, we showcase the effectiveness of Stella in geotagging images via two different scenarios: (1) we provide a graphical interface to show the process of a geotagging process that have been done by using Amazon Mechanical Turk, (2) we seek help from the conference attendees to propose an image to be geotagged or to help us geotag an image by using our application during the demonstration period. At the end of the demonstration period, we will show the geotagging result.

This paper presents GeoTrend; a system for scalable support of spatial trend discovery on recent microblogs, e.g., tweets and online reviews, that come in real time. GeoTrend is distinguished from existing techniques in three aspects: (1) It discovers trends in arbitrary spatial regions, e.g., city blocks. (2) It supports trending measures that effectively capture trending items under a variety of definitions that suit different applications. (3) It promotes recent microblogs as first-class citizens and optimizes its system components to digest a continuous flow of fast data in main-memory while removing old data efficiently. GeoTrend queries are top-k queries that discover the most trending k keywords that are posted within an arbitrary spatial region and during the last T time units. To support its queries efficiently, GeoTrend employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In case of peak times, a main-memory optimization technique is employed to shed less important data, so that the system still sustains high query accuracy with limited memory resources. Experimental results based on real Twitter feed and Bing Mobile spatial search queries show the scalability of GeoTrend to support arrival rates of up to 50,000 microblog/second, average query latency of 3 milli-seconds, and at least 90+% query accuracy even under limited memory resources.

Recently, many ride sharing systems have been commercially introduced (e.g., Uber, Flinc, and Lyft) forming a multi-billion dollars industry. The main idea is to match people requesting a certain ride to other people who are acting as drivers on their own spare time. The matching algorithm run by these services is very simple and ignores a wide sector of users who can be exploited to maximize the benefits of these services. In this demo, we demonstrate SHAREK; a driver-rider matching algorithm that can be embedded inside existing ride sharing services to enhance the quality of their matching. SHAREK has the potential to boost the performance and widen the user base and applicability of existing ride sharing services. This is mainly because within its matching technique, SHAREK takes into account user preferences in terms of maximum waiting time the rider is willing to have before being picked up as well as the maximum cost that the rider is willing to pay. Then, within its course of execution, SHAREK applies a set of smart filters that enable it to do the matching so efficiently without the need to many expensive shortest path computations.

Searching microblogs, e.g., tweets and comments, is practically supported through main-memory indexing for scalable data digestion and efficient query evaluation. With continuity and excessive numbers of microblogs, it is infeasible to keep data in main-memory for long periods. Thus, once allocated memory budget is filled, a portion of data is flushed from memory to disk to continuously accommodate newly incoming data. Existing techniques come with either low memory hit ratio due to flushing items regardless of their relevance to incoming queries or significant overhead of tracking individual data items, which limit scalability of microblogs systems in either cases. In this paper, we propose kFlushing policy that exploits popularity of top-k queries in microblogs to smartly select a subset of microblogs to flush. kFlushing is mainly designed to increase memory hit ratio. To this end, it identifies and flushes in-memory data that does not contribute to incoming queries. The freed memory space is utilized to accumulate more useful data that is used to answer more queries from memory contents. When all memory is utilized for useful data, kFlushing flushes data that is less likely to degrade memory hit ratio. In addition, kFlushing comes with a little overhead that keeps high system scalability in terms of high digestion rates of incoming fast data. Extensive experimental evaluation shows the effectiveness and scalability of kFlushing to improve main-memory hit by 26–330% while coping up with fast microblog streams of up to 100K microblog/second.

Microblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including detecting and analyzing events, user analysis for geo-targeted ads and political elections, and critical applications like discovering health issues and rescue services. Consequently, major research efforts are spent to analyze and manage microblogs data to support different applications. In this tutorial, we give a 1.5 hours overview about microblogs data analysis, management, and systems. The tutorial gives a comprehensive review for research efforts that are trying to analyze microblogs contents to build on them new functionality and use cases. In addition, the tutorial reviews existing research that propose core data management components to support microblogs queries at scale. Finally, the tutorial reviews system-level issues and on-going work on supporting microblogs data through the rising big data systems. Through its different parts, the tutorial highlights the challenges and opportunities in microblogs data research.

In this tutorial, we present the recent work in the database community for handling Big Spatial Data. This topic became very hot due to the recent explosion in the amount of spatial data generated by smartphones, satellites and medical devices, among others. This tutorial goes beyond the use of existing systems as-is (e.g., Hadoop, Spark or Impala), and digs deep into the core components of big systems (e.g., indexing and query processing) to describe how they are designed to handle big spatial data. During this 90-minute tutorial, we review the state-of-the-art work in the area of Big Spatial Data while classifying the existing research efforts according to the implementation approach, underlying architecture, and system components. In addition, we provide case studies of full-fledged systems and applications that handle Big Spatial Data which allows the audience to better comprehend the whole tutorial.

This paper introduces HadoopViz; a MapReduce-based framework for visualizing big spatial data. HadoopViz has three unique features that distinguish it from other techniques. (1) It exposes an extensible interface which allows users to define a new visualization types, e.g., scatter plot, road network, or heat map, by defining five abstract functions, without delving into the implementation details of the MapReduce algorithms. As it is open source, HadoopViz allows algorithm designers to focus on how the data should be visualized rather than performance or scalability issues. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase technique, partition-plot-merge. (3) HadoopViz provides a smoothing functionality which can fuse nearby records together as the image is plotted. This makes it capable of generating more types of images with high quality as compared to existing work. Experimental results on real datasets of up to 14 Billion points show the extensibility, scalability, and efficiency of HadoopViz to handle different visualization types of spatial big data.

The recent wide popularity of microblogs (e.g., tweets, online comments) has empowered various important applications, including, news delivery, event detection, market analysis, and target advertising. A core module in all these applications is a frequent/trending query processor that aims to find out those topics that are highly frequent or trending in the social media through posted microblogs. Unfortunately current attempts for such core module suffer from several drawbacks. Most importantly, their narrow scope, as they focus only on solving trending queries for a very special case of localized and very recent microblogs. This paper presents GARNET; a holistic system equipped with one-stop efficient and scalable solution for supporting a generic form of context-aware frequent and trending queries on microblogs. GARNET supports both frequent and trending queries, any arbitrary time interval either current, recent, or past, of fixed granularity, and having a set of arbitrary filters over contextual attributes. From a system point of view, GARNET is very appealing and industry-friendly, as one needs to realize it once in the system. Then, a myriad of various forms of trending and frequent queries are immediately supported. Experimental evidence based on a real system prototype of GARNET and billions of real Twitter data show the scalability and efficiency of GARNET for various query types.

Microblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including analyzing events and users activities and critical applications like discovering health issues and rescue services. Consequently, major research efforts are spent to manage, analyze, and visualize microblogs data to support different applications. In this tutorial, we give a 1.5 hours overview about microblogs data management, analysis, visualization, and systems. The tutorial gives a comprehensive review for research on core data management components to support microblogs queries at scale. This includes system-level issues and on-going work on supporting microblogs data through the rising wave of big data systems. In addition, the tutorial reviews research on microblogs data analysis and visualization. Through its different parts, the tutorial highlights the challenges and opportunities in microblogs data research.

In early 2000, we had the vision of ubiquitous location services, where each object is aware of its location, and continuously sends its location to a designated database server. This flood of location data opened the door for a myriad of location-based services that were considered visionary at that time, yet today they are a reality and have become ubiquitous. To realize our early vision, we identified two main challenges that needed to be addressed, namely, scalability and privacy. We have addressed these challenges through two main systems, PLACE and Casper. PLACE, developed at Purdue University from 2000 to 2005, set up the environment for built-in database support of scalable and continuous location-based services. The Casper system, developed at University of Minnesota from 2005 to 2010, was built inside the PLACE server allowing it to provide its high quality scalable service, while maintaining the privacy of its users' locations. This talk will take you through a time journey of location services from 2000 until today, and beyond, highlighting the development efforts of the PLACE and Casper systems, along with their impact on current and future research initiatives in both academia and industry.

Microblogging services have become among the most popular services on the web in the last few years. This led to significant increase in data size, speed, and applications. This paper presentsVenus; a system that supports real-time spatial queries on microblogs.Venussupports its queries on a spatial boundary$R$and a temporal boundary$T$, from which only the top-$k$microblogs are returned in the query answer based on a spatio-temporal ranking function. Supporting such queries requiresVenusto digest hundreds of millions of real-time microblogs in main-memory with high rates, yet, it provides low query responses and efficient memory utilization. To this end,Venusemploys: (1) an efficient in-memory spatio-temporal index that digests high rates of incoming microblogs in real time, (2) a scalable query processor that prune the search space,$R$and$T$, effectively to provide low query latency on millions of items in real time, and (3) a group of memory optimization techniques that provide system administrators with different options to save significant memory resources while keeping the query accuracy almost perfect.Venusmemory optimization techniques make use of the local arrival rates of microblogs to smartly shed microblogs that are old enough not to contribute to any query answer. In addition,Venuscan adaptively, in real time, adjust its load shedding based on both the spatial distribution and the parameters of incoming query loads. AllVenuscomponents can accommodate different spatial and temporal ranking functions that are able to capture the importance of each dimension differently depending on the applications requirements. Extensive experimental results based on real Twitter data and actual locations of Bing search queries show thatVenussupports high arrival rates of up to 64 K microblogs/second and average query latency of 4 msec.

Modern vehicles are increasingly being equipped with rich instrumentation that enables them to collect location aware data on a wide variety of travel related phenomena such as the real-world performance of engines and powertrain, driver preferences, context of the vehicle with respect to others nearby, and--indirectly--traffic on the transportation network itself. Combined with their increased access to the Internet, these connected vehicles are opening up vast opportunities to improve the safety, environmental friendliness, and the overall experience of urban travel. However, significant spatial computing challenges need to be addressed before we can realize the full potential of connected vehicles. This paper presents some of the open research questions under this theme from the perspectives of query processing, data science and data engineering.

This paper presents Sphinx, a full-fledged distributed system which uses a standard SQL interface to process big spatial data. Sphinx adds spatial data types, indexes and query processing, inside the code-base of Cloudera Impala for efficient processing of spatial data. In particular, Sphinx is composed of four main components, namely, query parser, indexer, query planner, and query executor. The query parser injects spatial data types and functions in the SQL interface of Sphinx. The indexer creates spatial indexes in Sphinx by adopting a two-layered index design. The query planner utilizes these indexes to construct efficient query plans for range query and spatial join operations. Finally, the query executor carries out these plans on big spatial datasets in a distributed cluster. A system prototype of Sphinx running on real datasets shows up-to three orders of magnitude performance improvement over traditional Impala.

This paper demonstrates Taghreed; a full-fledged system for efficient and scalable querying, analyzing, and visualizing geotagged microblogs, such as tweets. Taghreed supports a wide variety of queries on all microblogs attributes. In addition, it is able to manage a large number (billions) of microblogs for relatively long periods, e.g., months. Taghreed consists of four main components: (1) indexer, (2) query engine, (3) recovery manager, and (4) visualizer. Taghreed indexer efficiently digests incoming microblogs with high arrival rates in light main-memory indexes. When the memory becomes full, the memory contents are flushed to disk indexes which are managing billions of microblogs efficiently. On memory failure, the recovery manager restores the memory contents from backup copies. Taghreed query engine consists of two modules: a query optimizer and a query processor. The query optimizer generates an optimized query plan to be executed by the query processor to provide low query responses. Taghreed visualizer features to its users a wide variety of spatiotemporal queries and presents the answers on a map-based user interface that allows an interactive exploration. Taghreed is the first system that addresses all these challenges collectively for geotagged microblogs data. The system is demonstrated based on real system implementation through different scenarios that show system functionality and internals.

Several space agencies such as NASA are continuously collecting datasets of earth dynamics-e.g., temperature, vegetation, and cloud coverage-through satellites. This data is stored in a publicly available archive for scientists and researchers and is very useful for studying climate, desertification, and land use change. The benefit of this data comes from its richness as it provides an archived history for over 15 years of satellite observations. Unfortunately, the use of such data is very limited due to the huge size of archives (> 500TB) and the limited capabilities of traditional applications. In this demo, we present Shahed, an interactive system which provides an efficient way to index, query, and visualize satellite datasets available in NASA archive. Shahed is composed of four main modules. The uncertainty module resolves data uncertainty imposed by the satellites. The indexing module organizes the data in a novel multi-resolution spatio-temporal index designed for satellite data. The querying module uses the indexes to answer both spatiotemporal selection and aggregate queries provided by the user. The visualization module generates images, videos, and multi-level images which gives an insight of data distribution and dynamics over time. This demo gives users a hands-on experience with Shahed through a map-based web interface in which users can browse the available datasets using the map, issue spatiotemporal queries, and visualize the results as images or videos.

This paper describes SpatialHadoop; a full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and expressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. Other spatial operations are also implemented following a similar approach. Extensive experiments on real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

Remote sensing data collected by satellites are now made publicly available by several space agencies. This data is very useful for scientists pursuing research in several applications including climate change, desertification, and land use change. The benefit of this data comes from its richness as it provides an archived history for over 15 years of satellite observations for natural phenomena such as temperature and vegetation. Unfortunately, the use of such data is very limited due to the huge size of archives (> 500TB) and the limited capabilities of traditional applications. This paper introduces SHAHED; a MapReduce-based system for querying, visualizing, and mining large scale satellite data. SHAHED considers both the spatial and temporal aspects of the data to provide efficient query processing at large scale. The core of SHAHED is composed of four main components. The uncertainty component recovers missing data in the input which comes from cloud coverage and satellite mis-alignment. The indexing component provides a novel multi-resolution quad-tree-based spatio-temporal index structure, which indexes satellite data efficiently with minimal space overhead. The querying component answers selection and aggregate queries in real-time using the constructed index. Finally, the visualization component uses MapReduce programs to generate heat map images and videos for user queries. A set of experiments running on a live system deployed on a cluster of machines show the efficiency of the proposed design. All the features supported by SHAHED are made accessible through an easy to use web interface that hides the complexity of the system and provides a nice user experience.

Predictive queries on moving objects offer an important category of location-aware services based on the objects' expected future locations. A wide range of applications utilize this type of services, e.g., traffic management systems, location-based advertising, and ride sharing systems. This paper proposes a novel index structure, named Predictive tree (P-tree), for processing predictive queries against moving objects on road networks. The predictive tree: (1) provides a generic infrastructure for answering the common types of predictive queries including predictive point, range, KNN, and aggregate queries, (2) updates the probabilistic prediction of the object's future locations dynamically and incrementally as the object moves around on the road network, and (3) provides an extensible mechanism to customize the probability assignments of the object's expected future locations, with the help of user defined functions. The proposed index enables the evaluation of predictive queries in the absence of the objects' historical trajectories. Based solely on the connectivity of the road network graph and assuming that the object follows the shortest route to destination, the predictive tree determines the reachable nodes of a moving object within a specified time window T in the future. The predictive tree prunes the space around each moving object in order to reduce computation, and increase system efficiency. Tunable threshold parameters control the behavior of the predictive trees by trading the maximum prediction time and the details of the reported results on one side for the computation and memory overheads on the other side. The predictive tree is integrated in the context of the iRoad system in two different query processing modes, namely, the precomputed query result mode, and the on-demand query result mode. Extensive experimental results based on large scale real and synthetic datasets confirm that the predictive tree achieves better accuracy compared to the existing related work, and scales up to support a large number of moving objects and heavy predictive query workloads.

This paper advocates for the need to build a Microblogs Data Management System (MDMS) as an end-to-end data management system to support indexing, querying, and analyzing microblogs, e.g., Tweets, comments, or check-in's. We identify a set of characteristics for microblogging environments that are distinguishing from any other data management environment. Then, we propose a system architecture for the first Microblogs Data Management System, which includes indexing, querying, and recovery components. The indexing component is responsible for indexing recent data in memory, indexing older data in disk, and synchronizing the flow of data from memory to disk without affecting the query response time. The querying component is responsible for retrieving the query answer from both memory and disk storage as well as employing online selectivity estimation techniques tuned to the behavior of microblogs data. The recovery module allows for efficiently storing and processing incoming microblogs in memory without worrying about data loss.

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., Car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK, a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service and the maximum waiting time before being picked up. In the mean time, SHAREK computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK internally prunes those drivers that are dominated by others, i.e., They provide higher price and higher waiting time than other drivers. To realize its efficiency and scalability, SHAREK employs a set of early pruning techniques that minimize the need for any actual shortest path computations.

This seminar describes the state-of-the-art research in the area of big spatial data and it consists of four parts. Part I gives a background about big spatial data and the limitations of traditional systems in handling such data. Part II gives an overview of the four main components that constitute a system for big spatial data, namely, language, indexing, query processing, and visualization. In part III, we delve into the details of each of these four components and describe the recent research efforts in each component while highlighting the open research problems. Finally, part IV wraps up the seminar by describing case studies of systems and end-user applications for big spatial data. This part describes how the four components are implemented in these systems and how they are used in end-user applications.

This demo presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries. These queries are widely used in several applications including traffic management, location-based advertising, and store finders. Panda targets long-term query prediction as it relies on adapting a long-term prediction function to: (a) scale up to large number of moving objects, and (b) support predictive queries. Panda does not only aim to predict the query answer, but, it also aims to predict the incoming queries such that parts of the query answer can be precomputed before the query arrival. Panda maintains a tunable threshold that achieves a trade-off between the predictive query response time and the system overhead in precomputing the query answer. Equipped with a Graphical User Interface (GUI), audience can explore the Panda demo through issuing predictive queries over a moving set of objects on a map. In addition, they are able to follow the execution of such queries through an eye on the Panda execution engine.

This paper presents RECATHON, a context-aware recommender system built entirely inside a database system. Unlike traditional recommender systems that are context-free where they support the general query of Recommend movies for a certain user, RECATHON users can request recommendations based on their age, location, gender, or any other contextual/ demographical/preferential user attribute. A main challenge of supporting such kind of recommenders is the difficulty of deciding what attributes to build recommenders on. RECATHON addresses this challenge as it supports building recommenders in database systems in an analogous way to building index structures. Users can decide to create recommenders on selected attributes, e.g., Age and/or gender, and then entertain efficient support of multidimensional recommenders on the selected attributes. RECATHON employs a multi-dimensional index structure for each built recommender that can be accessed using novel query execution algorithms to support efficient retrieval for recommender queries. Experimental results based on an actual prototype of RECATHON, built inside Postgre SQL, using real Movie Lens and Foursquare data show that RECATHON exhibits real time performance for large-scale multidimensional recommendation.

SpatialHadoop is an extended MapReduce framework that supports global indexing that spatial partitions the data across machines providing orders of magnitude speedup, compared to traditional Hadoop. In this paper, we describe seven alternative partitioning techniques and experimentally study their effect on the quality of the generated index and the performance of range and spatial join queries. We found that using a 1% sample is enough to produce high quality partitions. Also, we found that the total area of partitions is a reasonable measure of the quality of indexes when running spatial join. This study will assist researchers in choosing a good spatial partitioning technique in distributed environments.

This demonstration presents HadoopViz; an extensible MapReduce-based system for visualizing Big Spatial Data. HadoopViz has two main unique features that distinguish it from other techniques. (1) It provides an extensible interface that allows users to visualize various types of data by defining five abstract functions, without delving into the details of the MapReduce algorithms. We show how it is used to create four types of visualizations, namely, scatter plot, road network, frequency heat map , and temperature heat map. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase approach of partitioning, rasterize , and merging. HadoopViz generates single and multi-level images, where the latter allows users to zoom in/out to get more/less details. Both types of images are generated with a very high resolution using the extensible and scalable framework of HadoopViz.

This paper presents Taghreed; a full-fledged system for efficient and scalable querying, analyzing, and visualizing geotagged microblogs, e.g., tweets. Taghreed supports arbitrary queries on a large number (Billions) of microblogs that go up to several months in the past. Taghreed consists of four main components: (f) Indexer, (2) query engine, (3) recovery manager, and (4) visualizer. Taghreed indexer efficiently digests incoming microblogs with high arrival rates in light memory-resident indexes. When the memory becomes full, a flushing policy manager transfers the memory contents to disk indexes which are managing Billions of microblogs for several months. On memory failure, the recovery manager restores the system status from replicated copies for the main-memory content. Taghreed query engine consists of two modules: a query optimizer and a query processor. The query optimizer generates an optimal query plan to be executed by the query processor through efficient retrieval techniques to provide low query response, i.e., order of milli-seconds. Taghreed visualizer allows end users to issue a wide variety of spatio-temporal queries. Then, it graphically presents the answers and allows interactive exploration through them. Taghreed is the first system that addresses all these challenges collectively for microblogs data. In the paper, each system component is described in detail.

Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not easily available for most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing and experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This paper presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap data set; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. Experimental results show that TAREEG is highly accurate and efficient.

In the last few years, Twitter data has become so popular that it is used in a rich set of new applications, e.g., real-time event detection, demographic analysis, and news extraction. As user-generated data, the plethora of Twitter data motivates several analysis tasks that make use of activeness of 271+ Million Twitter users. This demonstration presents VisCAT; a tool for aggregating and visualizing categorical attributes in Twitter data. VisCAT outputs visual reports that provide spatial analysis through interactive map-based visualization for categorical attributes---such as tweet language or source operating system---at different zoom levels. The visual reports are built based on user-selected data in arbitrary spatial and temporal ranges. For this data, VisCAT employs a hierarchical spatial data structure to materialize the count of each category at multiple spatial levels. We demonstrate VisCAT, using real Twitter dataset. The demonstration includes use cases on tweet language and tweet source attributes in the region of Gulf Arab states, which can be used for deducing thoughtful conclusions on demographics and living levels in local societies.

With the huge amounts of spatial data collected everyday, MapReduce frameworks, such as Hadoop, have become a common choice to analyze big spatial data for scientists and people from industry. Users prefer to use high level languages, such as Pig Latin, to deal with Hadoop for simplicity. Unfortunately, these languages are designed for primitive non-spatial data and have no support for spatial data types or functions. This demonstration presents Pigeon, a spatial extension to Pig which provides spatial functionality in Pig. Pigeon is implemented through user defined functions (UDFs) making it easy to use and compatible with all recent versions of Pig. This also allows it to integrate smoothly with existing non-spatial functions and operations such as Filter, Join and Group By. Pigeon is compatible with the Open Geospatial Consortium (OGC) standard which makes it easy to learn and use for users who are familiar with existing OGC-compliant tools such as PostGIS. This demonstrations shows to audience how to work with Pigeon through some interesting applications running on large scale real datasets extracted from OpenStreetMap.

Mars demonstration exploits the microblogs location information to support a wide variety of important spatio-temporal queries on microblogs. Supported queries include range, nearest-neighbor, and aggregate queries. Mars works under a challenging environment where streams of microblogs are arriving with high arrival rates. Mars distinguishes itself with three novel contributions: (1) Efficient in-memory digestion/expiration techniques that can handle microblogs of high arrival rates up to 64,000 microblog/sec. This also includes highly accurate and efficient hopping-window based aggregation for incoming microblogs keywords. (2) Smart memory optimization and load shedding techniques that adjust in-memory contents based on the expected query load to trade off a significant storage savings with a slight and bounded accuracy loss. (3) Scalable real-time query processing, exploiting Zipf distributed microblogs data for efficient top-k aggregate query processing. In addition, Mars employs a scalable real-time nearest neighbor and range query processing module that employs various pruning techniques so that it serves heavy query workloads in real time. Mars is demonstrated using a stream of real tweets obtained from Twitter firehose with a production query workload obtained from Bing web search. We show that Mars serves incoming queries with an average latency of less than 4 msec and with 99% answer accuracy while saving up to 70% of storage overhead for different query loads.

This paper presents Mercury; a system for real-time support of top-k spatio-temporal queries on microblogs, where users are able to browse recent microblogs near their locations. With high arrival rates of microblogs, Mercury ensures real-time query response within a tight memory-constrained environment. Mercury bounds its search space to include only those microblogs that have arrived within certain spatial and temporal boundaries, in which only the top-k microblogs, according to a spatio-temporal ranking function, are returned in the search results. Mercury employs: (a) a scalable dynamic in-memory index structure that is capable of digesting all incoming microblogs, (b) an efficient query processor that exploits the in-memory index through spatio-temporal pruning techniques that reduce the number of visited microblogs to return the final answer, (c) an index size tuning module that dynamically finds and adjusts the minimum index size to ensure that incoming queries will be answered accurately, and (d) a load shedding technique that trades slight decrease in query accuracy for significant storage savings. Extensive experimental results based on a real-time Twitter Firehose feed and actual locations of Bing search queries show that Mercury supports high arrival rates of up to 64K microblogs/second and average query latency of 4 msec.

This demo presents Minnesota Traffic Generator (MNTG); an extensible web-based road network traffic generator. MNTG enables its users to generate traffic data at any arbitrary road networks with different traffic generators. Unlike existing traffic generators that require a lot of time/effort to install, configure, and run, MNTG is a web service with a user-friendly interface where users can specify an arbitrary spatial region, select a traffic generator, and submit their traffic generation request. Once the traffic data is generated by MNTG, users can then download and/or visualize the generated data. MNTG can be extended to support: (1) various traffic generators. It is already shipped with the two most common traffic generators, Brinkhoff and BerlinMOD, but other generators can be easily added. (2) various road network sources. It is shipped with U.S. Tiger files and OpenStreetMap, but other sources can be also added. A beta version of MNTG is launched at: http://mntg.cs.umn.edu.

Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not really available in most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This demo presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap dataset; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. TAREEG is accessible via {http://www.tareeg.org/}

This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

News feed function becomes very popular in many social networking services and news aggregators, as it delivers the messages from users' subscribed sources. More recently, location has been introduced to the news feed function, which returns the news items relevant to the user's location. However, with the large number of the news items generated by the sources, existing news feed systems opt to return the top-k most recent ones, which completely overlooks the messages' spatial relevance and may end up in missing more geographically close ones. In this paper, we present GeoRank, an efficient location-aware news feed ranking system that provides top-k new feeds based on (a) spatial proximity, (b) temporal proximity, and (c) user preferences. GeoRank encapsulates spatio-temporal pruning techniques to improve its response time and efficiency. GeoRank is composed of two main modules, namely, query processor and message updater. The query processor module is triggered by the user, upon logging on to the system, to provide the top-k ranked location-based news feeds. The message updater module is a process running in the background, which keeps maintaining statistics used by the query processor module. Extensive experimental results, based on real and synthetic data sets, show the scalability and efficiency of GeoRank.

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG_Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.

In a business setting, the customer value is crucial as it determines how much it is worth spending to acquire a particular customer. Viral marketing techniques leverages social ties among users to help advertising a particular product. Recently, as mobile devices (e.g., smart phones, GPS devices) became ubiquitous, location-based social networking websites (e.g., Gowalla, BrightKite, Foursquare) are getting more and more popular. Along with location-based social networking services being prominent, new kind of data came into play besides the traditional social networking data: (1) Spatial data: represents the users geo-locations, venues geo-locations and information about users visiting different venues. (2) Users Opinions data: represents how much a user likes the venues she visits (e.g., Alice visited restaurant A and gave it a rating of five over five). In this paper, we present PLUTUS; a framework that assists venues (e.g., restaurant, gym, shopping mall) owners in growing their business. To recommend the best set of customers, PLUTUS takes three main aspects into consideration: (1) Social aspect, (2) Spatial aspect, and (3) Users opinions aspect. To this end, PLUTUS proposes two main algorithms: (1) Profit Calculation: It is responsible of calculating the total profit that a user u may add to a venue v taking into account the social, spatial, and user opinions aspects. (2) Profit Maximization: This algorithm is used to maximize the total profit of a given venue. We evaluated PLUTUS using real data set extracted from an existing Location-based Social Networking website, Foursquare. The results show that Plutus achieves higher estimated profit and more efficient profit calculation than naive marketing algorithms.

In the first part, we start by giving a quick overview of social networking services (e.g., Facebook, Twitter), their evolution, and how they impact the society. Similarly, we explain, through examples and case studies, how the widespread of mobile devices changed the computing paradigm in a way that impacted our daily life. In the second part, we present state-of-the-art research that lies within the intersection of both social networking and mobility, from the following perspectives: 1) geosocial queries; 2) geosocial recommendation; 3) geosocial analytics; 4) geosocial visualization; and 5) geosocial crowdsourcing.

This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k -NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.

This demo presents the iRoad framework for evaluating predictive queries on moving objects for road networks. The main promise of the iRoad system is to support a variety of common predictive queries including predictive point query, predictive range query, predictive KNN query, and predictive aggregate query. The iRoad framework is equipped with a novel data structure, named reachability tree, employed to determine the reachable nodes for a moving object within a specified future time Τ. In fact, the reachability tree prunes the space around each object in order to significantly reduce the computation time. So, iRoad is able to scale up to handle real road networks with millions of nodes, and it can process heavy workloads on large numbers of moving objects. During the demo, audience will be able to interact with iRoad through a well designed Graphical User Interface to issue different types of predictive queries on a real road network, to obtain the predictive heatmap of the area of interest, to follow the creation and the dynamic update of the reachability tree around a specific moving object, and finally to examine the system efficiency and scalability.

This tutorial presents the state-of-the-art research that lies at the intersection of two hot topics in the data management community: (1) social networking and (2) mobility. In this tutorial, we give an overview of existing research work, systems, and applications related to both social networking and mobility. In addition, we introduce several resources (i.e., datasets, software tools) as well as a list of promising research directions.

In this paper, we demonstrate RecDB; a full-fledged database system that provides personalized recommendation to users. We implemented RecDB using an existing open source database system PostgreSQL, and we demonstrate the effectiveness of RecDB using two existing recommendation applications (1) Restaurant Recommendation, (2) Movie Recommendation. To make the demo even more interactive, we showcase a novel application that recommends research papers presented at VLDB 2013 to the conference attendees based on their publication history in DBLP.

Horton+ is a graph query processing system that executes declarative reachability queries on a partitioned attributed multi-graph. It employs a query language, query optimizer, and a distributed execution engine. The query language expresses declarative reachability queries, and supports closures and predicates on node and edge attributes to match graph paths. We introduce three algebraic operators, select, traverse, and join, and a query is compiled into an execution plan containing these operators. As reachability queries access the graph elements in a random access pattern, the graph is therefore maintained in the main memory of a cluster of servers to reduce query execution time. We develop a distributed execution engine that processes a query plan in parallel on the graph servers. Since the query language is declarative, we build a query optimizer that uses graph statistics to estimate predicate selectivity. We experimentally evaluate the system performance on a cluster of 16 graph servers using synthetic graphs as well as a real graph from an application that uses reachability queries. The evaluation shows (1) the efficiency of the optimizer in reducing query execution time, (2) system scalability with the size of the graph and with the number of servers, and (3) the convenience of using declarative queries.

Most recommendation methods (e.g., collaborative filtering) consist of (1) a computationally intense offline phase that computes a recommender model based on users' opinions of items, and (2) an online phase consisting of SQL-based queries that use the model (generated offline) to derive user preferences and provide recommendations for interesting items. Current application usage trends require a completely online recommender process, meaning the recommender model must update in real time as new opinions enter the system. To tackle this problem, we propose RecStore, a DBMS storage engine module capable of efficient online model maintenance. Externally, models managed by RecStore behave as relational tables, thus existing SQL-based recommendation queries remain unchanged while gaining online model support. RecStore maintains internal statistics and data structures aimed at providing efficient incremental updates to the recommender model, while employing an adaptive strategy for internal maintenance and load shedding to realize a balance between efficiency in updates or query processing based on system workloads. RecStore is also extensible, supporting a declarative syntax for defining recommender models. The efficacy of RecStore is demonstrated by providing the implementation details of three state-of-the-art collaborative filtering models. We provide an extensive experimental evaluation of a prototype of RecStore, built inside the storage engine of PostgreSQL, using a real-life recommender system workload.

The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.

This paper presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries that are widely used in several applications including traffic management, location-based advertising, and ride sharing. Unlike previous attempts in supporting predictive queries, Panda targets long-term query prediction as it relies on adapting a well-designed long-term prediction function to: (a) scale up to large number of moving objects, and (b) support large number of predictive queries. As a means of scalability, Panda smartly precomputes parts of the most frequent incoming predictive queries, which significantly reduces the query response time. Panda employs a tunable threshold that achieves a trade-off between query response time and the maintenance cost of precomptued answers. Experimental results, based on large data sets, show that Panda is scalable, efficient, and as accurate as its underlying prediction function.

This paper presents the Geo Feed system, a location-aware news feed system that provides a new platform for its users to get spatially related message updates from either their friends or favorite news sources. Geo Feed distinguishes itself from all existing news feed systems in that it takes into account the spatial extents of messages and user locations when deciding upon the selected news feed. Geo Feed is equipped with three different approaches for delivering the news feed to its users, namely, spatial pull, spatial push, and shared push. Then, the main challenge of Geo Feed is to decide on when to use each of these three approaches to which users. Geo Feed is equipped with a smart decision model that decides about using these approaches in a way that: (a) minimizes the system overhead for delivering the location-aware news feed, and (b) guarantees a certain response time for each user to obtain the requested location-aware news feed. Experimental results, based on real and synthetic data, show that Geo Feed outperforms existing news feed systems in terms of response time and maintenance cost.

This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

This demo presents Sindbad; a location-based social networking system. Sindbad supports three new services beyond traditional social networking services, namely, location-aware news feed, location-aware recommender, and location-aware ranking. These new services not only consider social relevance for its users, but they also consider spatial relevance. Since location-aware social networking systems have to deal with large number of users, large number of messages, and user mobility, efficiency and scalability are important issues. To this end, Sindbad encapsulates its three main services inside the query processing engine of PostgreSQL. Usage and internal functionality of Sindbad, implemented with PostgreSQL and Google Maps API, are demonstrated through user (i.e., web/phone) and system analyzer GUI interfaces, respectively.

Social networks, mobility and the cloud represent special and unique opportunities for synergy among several existing and emerging communities that are now often evolving in isolated silos. All three areas hold much promise for the future of computing, and represent significant challenges for large scale data management. As these three areas evolve, their direct influence on significant decisions on each other becomes evident and critical. This panel will bring together a set of renowned researchers who will explore and discuss the synergy and tensions among critical and often intertwined research and application issues that arise in the context of social networks and mobility in a cloud infrastructure setting.

Preference queries are essential to a wide spectrum of applications including multi-criteria decision-making tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preference-aware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that filter out, from each input relation, those tuples that are guaranteed not to be in the final preference set, associate meta data with each non-filtered tuple that will be used to optimize the execution of the next phases, produce a subset of join result that are relevant for the given preference function, and refine these tuples respectively. An interesting characteristic of PrefJoin is that it tightly integrates preference computation with join hence we can early prune those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. PrefJoin supports a variety of preference function including skyline, multi-objective and k-dominance preference queries. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios.

Research and development of recommender systems has been a vibrant field for over a decade, having produced proven methods for “preference-aware” computing. Recommenders use community opinion histories to help users identify interesting items from a considerably large search space (e.g., inventory from Amazon [7], movies from Netflix [9]). Personalization, recommendation, and the “human side of data-centric applications are even becoming important topics in the data management community [3]. A popular recommendation method used heavily in practice is collaborative filtering, consisting of two phases: (1) An offline model-building phase that uses community opinions of items (e.g., movie ratings, “Diggs” [6]) to build a model storing meaningful correlations between users and items. (2) An on-demand recommendation phase that uses the model to produce a set of recommended items when requested from a user or application. To be effective, recommender systems must evolve with their content. In current update-intensive systems (e.g., social networks, online news sites), the restriction that a model be generated offline is a significant drawback, as it hinders the system’s ability to evolve quickly. For instance, new users enter the system changing the collective opinions over items, or the system adds new items quickly (e.g., news posts, Facebook postings), which widens the recommendation pool. These updates affect the recommender model, that in turn affect the system’s recommendation quality in terms of providing accurate answers to recommender queries. In such systems, a completely real-time recommendation process is paramount. Unfortunately, most traditional state-of-the-art recommenders are “hand-built, implemented as custom software not built for a real-time recommendation process [1]. Further, for some

This paper introduces an efficient framework for producing high and early result throughput in multijoin query plans. While most previous research focuses on optimizing for cases involving a single join operator, this work takes a radical step by addressing query plans with multiple join operators. The proposed framework consists of two main methods, a flush algorithm and operator state manager. The framework assumes a symmetric hash join, a common method for producing early results, when processing incoming data. In this way, our methods can be applied to a group of previous join operators (optimized for single-join queries) when taking part in multijoin query plans. Specifically, our framework can be applied by 1) employing a new flushing policy to write in-memory data to disk, once memory allotment is exhausted, in a way that helps increase the probability of producing early result throughput in multijoin queries, and 2) employing a state manager that adaptively switches operators in the plan between joining in-memory data and disk-resident data in order to positively affect the early result throughput. Extensive experimental results show that the proposed methods outperform the state-of-the-art join operators optimized for both single and multijoin query plans.

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm.

Most database systems allow query processing over attributes that are derived at query runtime (e.g., user-defined functions and remote data calls to web services), making them expensive to compute relative to relational data stored in a heap or index. In addition, core support for efficient preference query processing has become an important objective in database systems. This paper addresses an important problem at the intersection of these two query processing objectives: efficient preference query evaluation involving expensive attributes. We explore an efficient framework for processing skyline and multi-objective queries in a database when the data involves a mix of "cheap" and "expensive" attributes. Our solution involves a three-phase approach that evaluates a correct final preference answer while aiming to minimizing the number of expensive attributes computations. Unlike previous works for distributed preference algorithms that assume sorted access over each attribute, our framework assumes expensive attribute requests are stateless, i.e., know nothing previous requests. Thus, the proposed approach is more in line with realistic system architectures. Our framework is implemented inside the query processor of PostgreSQL, and evaluated over both synthetic and real data sets involving computation of expensive attributes over real web-service data (e.g., Microsoft MapPoint).

Personalized database systems give users answers tailored to their personal preferences. While numerous preference evaluation methods for databases have been proposed (e.g., skyline, top-k, k-dominance, k-frequency), the implementation of these methods at the core of a database system is a double-edged sword. Core implementation provides efficient query processing for arbitrary database queries, however this approach is not practical as each existing (and future) preference method requires a custom query processor implementation. To solve this problem, this paper introduces FlexPref, a framework for extensible preference evaluation in database systems. FlexPref, implemented in the query processor, aims to support a wide-array of preference evaluation methods in a single extensible code base. Integration with FlexPref is simple, involving the registration of only three functions that capture the essence of the preference method. Once integrated, the preference method ¿lives¿ at the core of the database, enabling the efficient execution of preference queries involving common database operations. To demonstrate the extensibility of FlexPref, we provide case studies showing the implementation of three database operations (single table access, join, and sorted list access) and five state-of-the-art preference evaluation methods (top-k, skyline, k-dominance, top-k dominance, and k-frequency). We also experimentally study the strengths and weaknesses of an implementation of FlexPef in PostgreSQL over a range of single-table and multi-table preference queries.

A k-Range Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for location-based applications in many realistic scenarios. For example, (1) the user’s location is uncertain, i.e., user’s location is modeled by a spatial region, and (2) the user is not willing to reveal her exact location to preserve her privacy, i.e., her location is blurred into a spatial region. However, the existing solutions for kRNN queries simply apply the traditional k-nearest neighbor query processing algorithm multiple times, which poses a huge redundant searching overhead. To this end, we propose an efficient kRNN query processing algorithm in this paper. Our algorithm (1) employs a shared execution approach to eliminate the redundant searching overhead, and (2) provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user’s exact k-nearest neighbors are included in the query answers. The experimental results show that our algorithm always outperforms the existing solution in terms of query response time, and the introduced tuning parameter is an effective way to achieve the tradeoff between the query response time and the storage overhead.

This demonstration presents FlexPref, a framework implemented inside the DBMS query processor that enables efficient and extensible preference query processing. FlexPref provides query processing support inside the database engine for a wide-array of preference evaluation methods (e.g., skyline, top-k, k-dominance, k-frequency) in a single extensible code base. Integration with FlexPref is simple, involving the registration of only three functions that capture the essence of the preference method. Once integrated, the preference method "lives" at the core of the database, enabling the efficient execution of preference queries involving common database operations (e.g, selection, join). Functionality of FlexPref, implemented inside PostgreSQL, is demonstrated through the implementation and use of several state-of-the-art preference methods in a real application scenario.

We demonstrate CareDB , a context and preference-aware database system. CareDB provides scalable personalized location-based services to users based on their preferences and current surrounding context. Unlike existing location-based database systems that answer queries based solely on proximity in distance, CareDB considers user preferences and various types of context in determining the answer to location-based queries. To this end, CareDB does not aim to define new location-based queries, instead, it aims to redefine the answer of existing location-based queries. To achieve its goals, CareDB has several distinguishing characteristics that revolve around a generic and extensible preference and context-aware query processing framework that addresses (a) scalable, efficient preference joins, (b) gracefully handling contextual attributes that are expensive to derive, and (c) support for uncertain attributes.

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general in that it is applicable for both continuous monochromatic and bichromatic reverse nearest neighbor queries. This problem is faced in a number of applications such as enhanced 911 services and in army strategic planning. A main challenge in these problems is to maintain the most up-to-date query answers as the data set frequently changes over time. Previous algorithms for monochromatic continuous reverse nearest neighbor queries rely mainly on monitoring at the worst case of six pie regions, whereas IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-the-art algorithms in monochromatic queries. We also propose a new optimization for the monochromatic IGERN to reduce the number of nearest neighbor searches. Furthermore, a filter and refine approach for IGERN (FR-IGERN) is proposed for the continuous evaluation of bichromatic reverse nearest neighbor queries which is an optimized version of our previous approach. The computational complexity of IGERN and FR-IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic and bichromatic cases. In addition, the correctness of IGERN and FR-IGERN in both the monochromatic and bichromatic cases, respectively, are proved. Extensive experimental analysis using synthetic and real data sets shows that IGERN and FR-IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

Context is any information used to characterize the situation of an entity. Examples of contexts include time, location, identity, and activity of a user. This paper proposes a general context-aware DBMS, named Chameleon, that will eliminate the need for having specialized database engines, e.g., spatial DBMS, temporal DBMS, and Hippocratic DBMS, since space, time, and identity can be treated as contexts in the general context-aware DBMS. In Chameleon, we can combine multiple contexts into more complex ones using the proposed context composition, e.g., a Hippocratic DBMS that also provides spatio-temporal and location contextual services. As a proof of concept, we construct two case studies using the same context-aware DBMS platform within Chameleon. One treats identity as a context to realize a privacy-aware (Hippocratic) database server, while the other treats space as a context to realize a spatial database server using the same proposed constructs and interfaces of Chameleon.

Location monitoring systems are used to detect human activities and provide monitoring services, e.g., aggregate queries. In this paper, we consider an aggregate location monitoring system where wireless sensor nodes are counting sensors that are only capable of detecting the number of objects within their sensing areas. As traditional query processors rely on the knowledge of users' exact locations, they cannot provide any monitoring services based on the readings reported from counting sensors. To this end, we propose an adaptive spatio-temporal histogram to enable monitoring services without the need of users' exact locations. The main idea of the histogram is to keep statistics about the distribution of moving objects. At the core of the histogram, we propose three techniques, memorization, locality awareness and packing, to improve monitoring accuracy and efficiency. Furthermore, the histogram is designed in a way that achieves a trade-off between the energy and bandwidth consumption of the sensor network and the accuracy of monitoring services. Experimental results show that the proposed histogram provides high-quality location monitoring services (i.e., 90% accuracy for both skewed and uniform mobility patterns) and outperforms a basic histogram and the state-of-the-art spatio-temporal histogram by two orders of magnitude in most cases.

In this paper, we propose a privacy model that offers trajectory privacy to the requesters of Location-Based Services (LBSs), by utilizing an underlying network of user movement. The privacy model has been implemented as a framework that (i) reconstructs the user movement from a series of independent location updates, (ii) identifies routes where user privacy is at risk, and (iii) anonymizes online user requests for LBSs to protect the requester for as long as the service withstands completion. In order to achieve (iii), we propose two anonymization techniques, the K–present (weak) and the K–frequent (strong) trajectory anonymity, and a second chance approach that takes over when anonymization fails to ensure that the privacy of the user is preserved. To the best of our knowledge, this is the first work to propose a trajectory privacy model that utilizes an underlying network of user movement to offer in an interactive way personalized privacy to online user requests on trajectory data.

To adapt database technology to new environments like cloud platforms or multi-core hardware, or to try anew to provide an extensible database platform, it is useful to separate transaction services from data management elements that need close physical proximity to data. With "generic" transactional services of concurrency control and recovery in a separate transactional component (TC), indexing, cache and disk management, now in a data component (DC), can be simplified and tailored more easily to the platform or to a data type extension with a special purpose index. This decomposition requires that details of the DC's management of data be hidden from the TC. Thus, locking and logging need to be "logical", which poses a number of problems. One problem is the handling of locking for ranges of keys. Locks need to be taken at the TC prior to the records and their keys being known to the TC. We describe generic two approaches for dealing with this. (1) Make a "speculative" visit" to the DC to learn key values. (2) Lock a "covering resource" first, then learn and lock key values and ultimately release the covering resource lock. The "table" is the only logical (and hence known to the TC) covering resourse in the traditional locking hierarchy, but using it limits concurrency. Concurrency is improved with the introduction of new partition resources. We show how partitions as covering resources combine high concurrency with low locking overhead. Using partitions is sufficiently effective to consider adapting it for a traditional database kernel.

In this article, we present a new privacy-aware query processing framework, Capser *, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user's cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations.

In applications like location-based services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the probabilistic nearest-neighbor query (PNN), which computes the probability of each object for being the nearest neighbor of a query point. Evaluating this query is computationally expensive, since it needs to consider the relationship among uncertain objects, and requires the use of numerical integration or Monte-Carlo methods. Sometimes, a query user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the constrained nearest-neighbor query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C-PNN can be answered efficiently with probabilistic verifiers. These are methods that derive the lower and upper bounds of answer probabilities, so that an object can be quickly decided on whether it should be included in the answer. We have developed three probabilistic verifiers, which can be used on uncertain data with arbitrary probability density functions. Extensive experiments were performed to examine the effectiveness of these approaches.

Recently, there has been much interest in processing skyline queries for various applications that include decision making, personalized services, and search pruning. Skyline queries aim to prune a search space of large numbers of multi dimensional data items to a small set of interesting items by eliminating items that are dominated by others. Existing skyline algorithms assume that all dimensions are available for all data items. This paper goes beyond this restrictive assumption as we address the more practical case of involving incomplete data items (i.e., data items missing values in some of their dimensions). In contrast to the case of complete data where the dominance relation is transitive, incomplete data suffer from non-transitive dominance relation which may lead to a cyclic dominance behavior. We first propose two algorithms, namely, "Replacement" and "Bucket" that use traditional skyline algorithms for incomplete data. Then, we propose the "ISkyline" algorithm that is designed specifically for the case of incomplete data. The "ISkyline" algorithm employs two optimization techniques, namely, virtual points and shadow skylines to tolerate cyclic dominance relations. Experimental evidence shows that the "ISkyline" algorithm significantly outperforms variations of traditional skyline algorithms.

This paper introduces an efficient algorithm for Producing Early Results in Multi-join query plans (PermJoin, for short). While most previous research focuses only on the case of a single join operator, PermJoin takes a radical step by addressing query plans with multiple join operators. PermJoin is optimized to maximize the early overall throughput and to adapt to fluctuations in data arrival rates. PermJoin is a non- blocking operator that is capable of producing join results even if one or more data sources are blocked due to slow or bursty network behavior. Furthermore, PermJoin distinguishes itself from all previous techniques as it: (1) employs a new flushing policy to write in-memory data to disk, once memory allotment is exhausted, in a way that helps increase the probability of producing early result throughput in multi-join queries, and (2) employs a novel state manager module that adaptively switches operators between joining in-memory data and disk-resident data in order to maximize overall throughput.

This demo presents a privacy-preserving aggregate location monitoring system, namely, TinyCasper, in which we can monitor moving objects in wireless sensor networks while preserving their location privacy. TinyCasper consists of two main modules, in-network location anonymization and aggregate query processing over anonymized locations. In the first module, trusted wireless sensor nodes collaborate with each other to anonymize users' exact locations by a cloaked spatial region that satisfies a prespecified privacy requirement. On the other side, the aggregate query processing module collects and analyzes the cloaked spatial regions reported from the wireless sensor nodes to support aggregate and alarm queries over anonymized locations. The prototype of TinyCasper is implemented on a physical test-bed on the TinyOS/Mote platform with 39 MICAz motes.

Applications that create and consume unstructured data have grown both in scale of storage requirements and complexity of search primitives. We consider two such applications: exhaustive search and integration of structured and unstructured data. Current block-based storage systems are either incapable or inefficient to address the challenges bought forth by the above applications. We propose a storage framework to efficiently store and search unstructured and structured data while controlling storage management costs. Experimental results based on our prototype show that the proposed system can provide impressive performance and feature benefits.

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-the-art algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

This demo presents Casper; a framework in which users entertain anonymous location-based services. Casper consists of two main components; the location anonymizer that blurs the users' exact location into cloaked spatial regions and the privacy-aware query processor that is responsible on providing location-based services based on the cloaked spatial regions. While the location anonymizer is implemented as a stand alone application, the privacy-aware query processor is embedded into PLACE; a research prototype for location-based database servers.

Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Several research efforts are dedicated to detect and track various phenomena inside a data stream management system (DSMS). In this paper, we use the detected phenomena to reduce the demand on the DSMS resources. The main idea is to let the query processor observe the input data streams at the phenomena level. Then, each incoming continuous query is directed only to those phenomena that participate in the query answer. Two levels of indexing are employed, a phenomenon index and a query index. The phenomenon index provides a fine resolution view of the input streams that participate in a particular phenomenon. The query index utilizes the phenomenon index to maintain a query deployment map in which each input stream is aware of the set of continuous queries that the stream contributes to their answers. Both indices are updated dynamically in response to the evolving nature of phenomena and to the mobility of the stream sources. Experimental results show the efficiency of this approach with respect to the accuracy of the query result and the resource utilization of the DSMS.

The explosive growth of location-detection devices (e.g., GPS-like devices and handheld devices) along with wireless communications and mobile databases results in realizing location-based applications that deliver specific information to their users based on their current locations. Examples of such applications include location-based store finder, location-based traffic reports, and location-based advertisements. Although location-based services promise safety and convenience, they threaten the privacy and security of users as such services explicitly require users to share private location information with the service. If a user wants to keep her location information private, she has to turn off her location-aware device and temporarily unsubscribe from the service. Recent studies show that such privacy concerns - ranging from worries over employers snooping on their workers' whereabouts to fears of tracking by potential stalkers - are a serious obstacle to wider adoption of location-based services. This article aims to provide practitioners, researchers, and graduate students with the state of the art and major research issues in the important and practical research area of location privacy. In general, the tutorial is divided into the following five parts: (1) legislative issues and privacy concerns, (2) location privacy in mobile environments, (3) privacy attack models, (4) privacy-aware location query processing; (5) concluding remarks.

The wide spread use of cellular phones, handheld devices, and GPS-like technology enables location-aware environments where virtually all objects are aware of their locations. Such environments call for new query processing techniques that deal with the continuous movement of both spatio-temporal objects and queries. The goal of this tutorial is to: (1) Give an in-depth view on supporting location-aware queries as an increasingly interesting area of research, (2) Present the state-of-the-art techniques for efficient handling of location-aware snapshot/continuous queries, and (3) Motivate the need for integrating location-awareness as a new query processing and optimization dimension, and (4) Raise several research challenges that need to be addressed towards a true support for location-aware queries in database management systems.

Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query revaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations

This paper tackles a major privacy threat in current location-based services where users have to report their exact locations to the database server in order to obtain their desired services. For example, a mobile user asking about her nearest restaurant has to report her exact location. With untrusted service providers, reporting private location information may lead to several privacy threats. In this paper, we present a peer-to-peer (P2P)spatial cloaking algorithm in which mobile and stationary users can entertain location-based services without revealing their exact location information. The main idea is that before requesting any location-based service, the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing. Then,the spatial cloaked area is computed as the region that covers the entire group of peers. Two modes of operations are supported within the proposed P2P s patial cloaking algorithm, namely, the on-demand mode and the proactive mode. Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode, but the on-demand incurs longer response time.

Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the "clock times" of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.

Indexing moving objects is a fundamental issue in spatiotemporal databases. In this paper, we propose an adaptive Lazy-Update Grid-based index (LUGrid, for short) that minimizes the cost of object updates. LUGrid is designed with two important features, namely, lazy insertion and lazy deletion. Lazy insertion reduces the update I/Os by adding an additional memory-resident layer over the disk index. Lazy deletion reduces update cost by avoiding deleting single obsolete entry immediately. Instead, the obsolete entries are removed later by specially designed mechanisms. LUGrid adapts to object distributions through cell splitting and merging. Theoretical analysis and experimental results indicate that LUGrid outperforms former work by up to eight times when processing intensive updates, while yielding similar search performance.

Location-aware environments are characterized by a large number of objects and a large number of continuous queries. Both the objects and continuous queries may change their locations over time. In this paper, we focus on continuous k-nearest neighbor queries (CKNN, for short). We present a new algorithm, termed SEA-CNN, for answering continuously a collection of concurrent CKNN queries. SEA-CNN has two important features: incremental evaluation and shared execution. SEA-CNN achieves both efficiency and scalability in the presence of a set of concurrent queries. Furthermore, SEA-CNN does not make any assumptions about the movement of objects, e.g., the objects velocities and shapes of trajectories, or about the mutability of the objects and/or the queries, i.e., moving or stationary queries issued on moving or stationary objects. We provide theoretical analysis of SEA-CNN with respect to the execution costs, memory requirements and effects of tunable parameters. Comprehensive experimentation shows that SEA-CNN is highly scalable and is more efficient in terms of both I/O and CPU costs in comparison to other R-tree-based CKNN techniques.

This paper introduces a new family of Generic and Progressive algorithms (GPAC, for short) for continuous mobile queries over mobile objects. GPAC provides a general skeleton that can be tuned through a set of methods to behave as various continuous queries (e.g., continuous range queries and continuous k-nearest-neighbor queries). GPAC algorithms aim to provide three goals: (1) Online evaluation through an in-memory processing of the incoming mobile data. (2) Progressive evaluation through employing an incremental evaluation paradigm. (3) Fast query response through employing an anticipation paradigm. Query answer is anticipated and is cached in memory to allow for fast evaluation. GPAC algorithms are encapsulated in physical pipelined query operators. GPAC pipelined operators can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Experimental results based on a real implementation inside a prototype streaming database engine show the efficiency of GPAC operators in providing incremental and fast response for continuous queries.

Immortal DB builds transaction time database support into the SQL Server engine, not in middleware. Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. The system supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the times of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to all updates of a transaction after commit. All versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing. We demonstrate Immortal DB with a moving objects application that tracks cars in the Seattle area.

The emergence of extensible index structures, e.g., GiST (generalized search tree) [J.M. Hellerstein et al. (1995)] and SP-GiST (space-partitioning generalized search tree) [W. G Aref et al., (2001)], calls for a set of extensible algorithms to support different operations (e.g., insertion, deletion, and search). Extensible bulk operations (e.g., bulk loading and bulk insertion) are of the same importance and need to be supported in these index engines. In this paper, we propose two extensible buffer-based algorithms for bulk operations in the class of space-partitioning trees; a class of hierarchical data structures that recursively decompose the space into disjoint partitions. The main idea of these algorithms is to build an in-memory tree of the target space-partitioning index. Then, data items are recursively partitioned into disk-based buffers using the in-memory tree. Although the second algorithm is designed for bulk insertion, it can be used in bulk loading as well. The proposed extensible algorithms are implemented inside SP-GiST; a framework for supporting the class of space-partitioning trees. Both algorithms have I/O bound O(NH/B), where N is the number of data items to be bulk loaded/inserted, B is the number of tree nodes that can fit in one disk page, H is the tree height in terms of pages after applying a clustering algorithm. Experimental results are provided to show the scalability and applicability of the proposed algorithms for the class of space-partitioning trees. A comparison of the two proposed algorithms shows that the first algorithm performs better in case of bulk loading. However the second algorithm is more general and can be used for efficient bulk insertion.

We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the query processing engine, "Nile". Nile extends the query processor engine of an object-relational database management system, PREDATOR, to process continuous queries over data streams. Nile supports extended SQL operators that handle sliding-window execution as an approach to restrict the size of the stored state in operators such as join.

A new multimedia disk-scheduling algorithm, termed Cascaded-SFC, is presented. The Cascaded-SFC multimedia disk scheduler is applicable in environments where multimedia data requests arrive with different quality of service (QoS) requirements such as real-time deadline and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. The Cascaded-SFC disk scheduler provides a unified framework for multimedia disk scheduling that scales with the number of scheduling parameters. The general idea is based on modeling the multimedia disk requests as points in multiple multidimensional subspaces, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number, and a third dimension represents the priority of the request, etc.). Each multidimensional subspace represents a subset of the QoS parameters that share some common scheduling characteristics. Then the multimedia disk scheduling problem reduces to the problem of finding a linear order to traverse the multidimensional points in each subspace. Multiple space-filling curves are selected to fit the scheduling needs of the QoS parameters in each subspace. The orders in each subspace are integrated in a cascaded way to provide a total order for the whole space. Comprehensive experiments demonstrate the efficiency and scalability of the Cascaded-SFC disk scheduling algorithm over other disk schedulers.

We introduce the hash-merge join algorithm (HMJ, for short); a new nonblocking join algorithm that deals with data items from remote sources via unpredictable, slow, or bursty network traffic. The HMJ algorithm is designed with two goals in mind: (1) minimize the time to produce the first few results, and (2) produce join results even if the two sources of the join operator occasionally get blocked. The HMJ algorithm has two phases: The hashing phase and the merging phase. The hashing phase employs an in-memory hash-based join algorithm that produces join results as quickly as data arrives. The merging phase is responsible for producing join results if the two sources are blocked. Both phases of the HMJ algorithm are connected via a flushing policy that flushes in-memory parts into disk storage once the memory is exhausted. Experimental results show that HMJ combines the advantages of two state-of-the-art nonblocking join algorithms (XJoin and Progressive Merge Join) while avoiding their shortcomings.

This paper intoduces the Scalable INcremental hash-based Algorithm (SINA, for short); a new algorithm for evaluting a set of concurrent continuous spatio-temporal queries. SINA is designed with two goals in mind: (1) Scalability in terms of the number of concurrent continuous spatio-temporal queries, and (2) Incremental evaluation of continyous spatio-temporal queries. SINA achieves scalability by empolying a shared execution paradigm where the execution of continuous spatio-temporal queries is abstracted as a spatial join between a set of moving objects and a set of moving queries. Incremental evaluation is achived by computing only the updates of the previously reported answer. We introduce two types of updaes, namely positive and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. SINA manages the computation of postive and negative updates via three phases: the hashing phase, the invalidation phase, and the joining phase. the hashing phase employs an in-memory hash-based join algorithm that results in a set a positive upldates. The invalidation phase is triggered every T seconds or when the memory is fully occupied to produce a set of negative updates. Finally, the joining phase is triggered by the end of the invalidation phase to produce a set of both positive and negative updates that result from joining in-memory data with in-disk data. Experimental results show that SINA is scalable and is more efficient than other index-based spatio-temporal algorithms.

The emergence of location-aware services calls for new real time spatio-temporal query processing algorithms that deal with large numbers of mobile objects and queries. Online query response is an important characterization of location-aware services. A delay in the answer to a query gives invalid and obsolete results, simply because moving objects can change their locations before the query responds. To handle large numbers of spatio-temporal queries efficiently, we propose the idea of sharing as a means to achieve scalability. In this paper, we introduce several types of sharing in the context of continuous spatio-temporal queries. Examples of sharing in the context of real-time spatio-temporal database systems include sharing the execution, sharing the underlying space, sharing the sliding time windows, and sharing the objects of interest. We demonstrate how sharing can be integrated into query predicates, e.g., selection and spatial join processing. The goal of this paper is to outline research directions and approaches that will lead to scalable and efficient location-aware services.

For the past two decades, fractals (e.g., the Hilbert and Peano space-filling curves) have been considered the natural method for providing a locality-preserving mapping. The idea behind a locality-preserving mapping is to map points that are nearby in the multidimensional space into points that are nearby in the one-dimensional space. We argue against the use of fractals in locality-preserving mapping algorithms, and present examples with experimental evidence to show why fractals produce poor locality-preserving mappings. In addition, we propose an optimal locality-preserving mapping algorithm, termed the spectral locality-preserving mapping algorithm (Spectral LPM, for short), that makes use of the spectrum of the multidimensional space. We give a mathematical proof for the optimality of Spectral LPM, and also demonstrate its practical use.

A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as real-time deadline, and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. A unified framework for QoS disk scheduling is presented that scales with the number of scheduling parameters. The general idea is based on modeling the disk scheduler requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number and a third dimension represents the priority of the request, etc.). Then the disk scheduling problem reduces to the problem of finding a linear order to traverse these multi-dimensional points. Space-filling curves are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one-dimensional disk scheduling algorithms (e.g., EDF SATF, FIFO). Several techniques are presented to show how a QoS-aware disk scheduler deals with the progressive arrival of requests over time. Simulation experiments are presented to show a comparison of the alternative techniques and to demonstrate the scalability of the proposed QoS-aware disk scheduling algorithm over other traditional approaches.

A space-filling curve is a way of mapping the multi-dimensional space into the one-dimensional space. It acts like a thread that passes through every cell element (or pixel) in the N-dimensional space so that every cell is visited at least once. Thus, a space-filling curve imposes a linear order of the cells in the N-dimensional space. There are numerous kinds of space-filling curves. The difference between such curves is in their way of mapping to the one-dimensional space. Selecting the appropriate curve for any application requires a brief knowledge of the mapping scheme provided by each space-filling curve. Irregularity is proposed as a quantitative measure of the quality of the mapping of the space-filling curve. Closed formulas are developed to compute the irregularity for any general dimension D with N points in each dimension for different space-filling curves.A comparative study of different space-filling curves with respect to irregularity is conducted and results are presented and discussed. The applicability of this research is the area of multimedia databases is illustrated with a discussion of the problems that arise.