Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE , a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device–model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology—allowing EVs to also discharge energy back to the grid—new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximisation problem where EV drivers must select a subset of customer requests while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighbourhood search (LNS). We compare these three algorithms with a greedy baseline on real-world data, showing that the proposed methods achieve up to twice the profit. V2G contributes about 20 % of the total profit in the default settings. MIP finds optimal solutions for small cases (30 orders, 3 stations) but does not scale well. EA and LNS give near-optimal results for small cases and handle large ones (900 orders, 70 stations) efficiently. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

With the rise of electric vehicles (EVs), new opportunities are emerging, including Vehicle-to-Everything (V2X) technology, which enables EVs to both charge from and discharge to the grid, homes, and other EVs. Leveraging V2X, EVs can act as "batteries-on-wheels," dynamically trading energy to minimize electricity costs and emissions based on real-time energy and mobility forecasts. For households, unlocking these benefits requires smart, automated management of EV charging and discharging. However, designing optimal schedules is a complex task involving dynamic and often uncertain variables such as emission rates, household electricity use, solar generation, electricity prices, and EV travel patterns. To tackle this challenge, we have developed an interactive dashboard that combines forecasting and optimization to support smarter energy decisions. To the best of our knowledge, this is the first system that integrates real-world minute-level forecasts, dynamic scheduling algorithms, and interactive spatiotemporal visualization. It enables users to compare V2X scenarios and explore the impact of forecasting accuracy and configuration strategies over time. The dashboard visualizes key variables and simulates optimal charging and discharging decisions. In this demo, we showcase results from a 31-day simulation at 5-minute intervals, using real-world data to illustrate the impact of various energy management strategies.

Public transportation plays a vital role in supporting sustainable, accessible, environment-friendly, and equitable urban mobility. However, challenges such as poor first- and last-mile connectivity, limited service coverage, and inefficient use of space continue to limit its effectiveness and uptake. Autonomous vehicles (AVs) offer new opportunities to address these limitations by enhancing flexibility, improving access, and complementing existing transit systems. This vision paper explores how a diverse fleet of AVs, including cars, shuttles, pods, scooters, and buses, can be integrated into public transport to form an adaptive, multimodal, and data-driven mobility ecosystem. We outline key research directions spanning fleet coordination, spatial deployment, infrastructure planning, and intelligent transportation platforms. We highlight the need for interdisciplinary research at the intersection of spatial computing, transportation systems, artificial intelligence, and urban data infrastructure. Our aim is to inform and inspire future efforts toward building autonomous mobility systems that are efficient, inclusive, and future-ready.

Electric Vehicles (EVs) are increasingly recognized not only as key assets for sustainable transportation but also as flexible, distributed energy resources. This dual role is enabled by the emergence of Vehicle-to-Everything (V2X) technologies, which allow EVs to bidirectionally charge and discharge energy across various domains, such as the grid, homes, buildings, other vehicles, and mobile devices. As global momentum builds toward decarbonizing both transportation and energy systems, the integration of V2X positions EVs at the intersection of these domains, offering new opportunities to enhance energy efficiency, grid resilience, and environmental sustainability. This tutorial provides a comprehensive introduction to the potential of EVs as both transportation and energy storage solutions, focusing specifically on practical applications and recent advancements in V2X integration. Participants will explore foundational concepts and practical use cases across individual and fleet scenarios, including energy-aware EV routing, smart charging, and coordinated energy management. By bridging transportation and energy domains, the tutorial offers participants insights into leveraging EVs to enhance mobility, resilience, and energy efficiency.

Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we introduce MapEval, a benchmark designed to assess foundation models across three distinct tasks—textual, API-based, and visual reasoning—through 700 multiple-choice questions spanning 180 cities and 54 countries, covering spatial relationships, navigation, travel planning, and real-world map interactions. Unlike prior benchmarks that focus on simple location queries, MapEval requires models to handle long-context reasoning, API interactions and visual map analysis, making it the most comprehensive evaluation framework for geospatial AI. On evaluation of 30 foundation models, including Claude-3.5-Sonnet, GPT-4o, Gemini-1.5-Pro, none surpasses 67% accuracy, with open-source models performing significantly worse and all models lagging over 20% behind human performance. These results expose critical gaps in spatial inference, as models struggle with distances, directions, route planning, and place-specific reasoning, highlighting the need for better geospatial AI to bridge the gap between foundation models and real-world navigation.

The increasing proliferation of loT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and cloud resources to meet this need, balancing the low-latency demands of the edge with the high computational power of the cloud. However, managing resources in such a distributed environment presents challenges due to the diversity and complexity of these systems. Traditional resource management methods, often relying on heuristic algorithms, struggle to manage the increasing complexity, scale, and dynamics of these systems, as well as adapt to dynamic workloads and changing network conditions. Moreover, designing such approaches is often time-intensive and highly tailored to specific applications, demanding deep expertise. In this paper, we introduce a novel framework for intent-driven resource management in the compute continuum, using large language models (LLMs) to help automate decision-making processes. Our framework ensures that user-defined intents - such as achieving the required response times for time-critical applications - are consistently fulfilled. In the event of an intent violation, our system performs root cause analysis by examining system data to identify and address issues. This approach reduces the need for human intervention and enhances system reliability, offering a more dynamic and efficient solution for resource management in distributed environments.

Electric vehicles (EVs) have the potential to serve as energy storage solutions through bidirectional charging technology, which allows them to both draw power from and feed power back into the grid, homes, or other vehicles. This capability enables EVs to reduce emissions, optimize costs, and support the grid by storing energy during periods of high production and supplying it when demand is high. In this vision paper, we focus on unlocking the potential of EVs as energy storage solutions while ensuring they remain readily available for transportation, their primary purpose. A significant research gap exists in that most current studies prioritize energy management, often using simplistic approaches that inadequately address the travel needs of EV owners. We believe the database community can be instrumental in maximizing the dual role of EVs as transportation and energy storage. We present a non-exhaustive list of research directions for various EV stakeholders, including individual EV owners, groups of independent yet cooperative EVs, commercial EV fleets, and autonomous EVs, and hope to inspire the database community for further exploration.

Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object$o$, e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with$o$for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals.

Serverless edge systems simplify the deployment of real-time AI-based Internet of Things (IoT) applications at the edge. However, the heterogeneity of edge computing nodes – in terms of both hardware and software – makes load balancing challenging in these systems. In this paper, we propose a performance-driven, empirical weight-tuning approach to achieve effective load balancing based on the characteristics and capabilities of the nodes. By extensively profiling the nodes, we gather knowledge on performance metrics such as throughput, energy efficiency, response time, AI accuracy, and cost. Using this acquired knowledge, we introduce a weighted round-robin strategy to optimize the performance metrics according to their observed significance. To address multiple objectives, we introduce a multi-objective method that aims to strike a balance between any arbitrary set of performance objectives simultaneously. Additionally, we explore a coordinated distributed approach to overcome the limitations of centralized load balancing. Next, we introduce Hedgi, a heterogeneous serverless edge architecture designed to efficiently configure and utilize the derived load balancing policies, validated empirically. To demonstrate the practicality of Hedgi, we containerize and serverlessize a real-time object detection application. Extensive empirical studies are conducted using Hedgi to evaluate the performance of the proposed load balancing approach. The results provide valuable insights into the design trade-offs of various load balancing policies and system designs in the heterogeneous serverless edge.

Reverse Approximate Nearest Neighbor (RANN) query relaxes the RkNN definition of influence, where a user u can be influenced by not only its closest facility but also by every other facility that is almost as close to u as its closest facility is. In this paper, we study the continuous monitoring of RANN queries on road network. Existing continuous RANN algorithms on Euclidean space cannot be extended to continuously monitor RANN queries on road network. We propose two different methods to efficiently monitor RANN queries. We conduct an extensive experiment on different real data sets and demonstrate that our both proposed algorithms are significantly better than the competitor

Guest Editorial: Special Issue on Generating Human Readable Explanations in NLP

Alternative route planning requires finding$k$alternative paths (including the shortest path) between a given source and target. These paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While there exists many work on finding high-quality alternative paths, the existing techniques are computationally expensive and are unable to accommodate the high volume of queries required by modern navigation systems. To address this, in this paper, we propose an efficient approach to compute high-quality alternative paths. Our approach employs hub-labeling to efficiently identify candidate alternative paths. The candidate paths are then ranked considering multiple quality metrics and the top-$k$alternative paths are returned. We propose several non-trivial optimizations to significantly improve the computation time. In our experimental study, we conduct experiments on three diverse real-world road networks and compare our proposed algorithm against six state-of-the-art algorithms. The results demonstrate that our algorithm is not only up to 3 orders of magnitude faster compared to most algorithms but also consistently generates alternative paths that are comparable or even superior in terms of quality across various metrics.

Serverless edge computing is a specialized system design tailored for Internet of Things (IoT) applications. It leverages serverless computing to minimize operational management and enhance resource efficiency, and utilizes the concept of edge computing to allow code execution near the data sources. However, edge devices powered by renewable energy face challenges due to energy input variability, resulting in imbalances in their operational availability. As a result, high-powered nodes may waste excess energy, while lowpowered nodes may frequently experience unavailability, impacting system sustainability. Addressing this issue requires energy-aware resource schedulers, but existing cloud-native serverless frameworks are energy-agnostic. To overcome this, we propose an energyaware scheduler for sustainable serverless edge systems. We introduce a reference architecture for such systems and formally model energy-aware resource scheduling, treating the function-to-node assignment as an imbalanced energy-minimizing assignment problem. We then design an optimal offline algorithm and propose faasHouse, an online energy-aware scheduling algorithm that utilizes resource sharing through computation offloading. Lastly, we evaluate faasHouse against benchmark algorithms using real-world renewable energy traces and a practical cluster of single-board computers managed by Kubernetes. Our experimental results demonstrate significant improvements in balanced operational availability (by 46%) and throughput (by 44%) compared to the Kubernetes scheduler.

Finding shortest paths in a Euclidean plane containing polygonal obstacles is a well-studied problem motivated by a variety of real-world applications. The state-of-the-art algorithms require finding obstacle corners visible to the source and target, and need to consider potentially a large number of candidate paths. This adversely affects their query processing cost. We address these limitations by proposing a novel adaptation of hub labeling which is the state-of-the-art approach for shortest distance computation in road networks. Our experimental study conducted on the widely used benchmark maps shows that our approach is typically 1-2 orders of magnitude faster than two state-of-the-art algorithms.

The advancement of indoor location-aware technologies enables a wide range of location based services in indoor spaces. In this paper, we formulate a novel Indoor Facility Location Selection (IFLS) query that finds the optimal location for placing a new facility (e.g., a coffee station) in an indoor venue (e.g., a university building) such that the maximum distance of all clients (e.g., staffs/students) to their nearest facility is minimized. To the best of our knowledge we are the first to address this problem in an indoor setting. We first adapt the state-of-the-art solution in road networks for indoor settings, which exposes the limitations of existing approaches to solve our problem in an indoor space. Therefore, we propose an efficient approach which prunes the search space in terms of the number of clients considered, and the total number of facilities retrieved from the database, thus reducing the total number of indoor distance calculations required. The key idea of our approach is to use a single pass on a state-of-the-art index for an indoor space, and reuse the nearest neighbor computation of clients to prune irrelevant facilities and clients. We evaluate the performance of both approaches on four indoor datasets. Our approach achieves a speedup from 2.84× to 71.29× for synthetic data and 97.74× for real data over the baseline.

Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.

The rapid development of emerging vehicular edge computing (VEC) brings new opportunities and challenges for dynamic resource management. The increasing number of edge data centers, roadside units (RSUs), and network devices, how-ever, makes resource management a complex task in VEC. On the other hand, the exponential growth of service applications and end-users makes corresponding QoS hard to maintain. Intent-Based Networking (IBN), based on Software-Defined Networking, was introduced to provide the ability to automatically handle and manage the networking requirements of different applications. Motivated by the IBN concept, in this paper, we propose a novel approach to jointly orchestrate networking and computing resources based on user requirements. The proposed solution constantly monitors user requirements and dynamically re-configures the system to satisfy desired states of the application. We compared our proposed solution with the state-of-the-art networking embedding algorithms using real-world taxi GPS traces. Results show that our proposed method is significantly faster (up to 95%) and can improve resource utilization (up to 76%) and the acceptance ratio of computing and networking requests with various priorities (up to 71%). We also present a small-scale prototype of the proposed intent management framework to validate our solution.

Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.

Safety on the roads has become a major concern in recent days. Travellers prefer to avoid road inconveniences that may occur from crime incidents, street harassment, protests or riots during unrest in a country. To facilitate safe travel, we introduce a novel query for road networks called the$k$safest nearby neighbor (SNN) query. Given a query location$v_{l}$, a distance constraint$d_{c}$and a point of interest$p_{i}$, we define the safest path from$v_{l}$to$p_{i}$as the path with the highestpath safety scoreamong all the paths from$v_{l}$to$p_{i}$with length less than$d_{c}$. The path safety score is computed considering the road safety of each road segment on the path. Given a query location$v_{l}$, a distance constraint$d_{c}$and a set of POIs$P$, a$k$SNN query returns$k$POIs with the$k$highest path safety scores in$P$along with their respective safest paths from the query location. We develop two novel indexing structures called$Ct$-tree and a safety score based Voronoi diagram (SNVD). We propose two efficient query processing algorithms each exploiting one of the proposed indexes to effectively refine the search space using the properties of the index. Our extensive experimental study on real datasets demonstrates that our solution is on average an order of magnitude faster than the baseline.

The recent years have witnessed the growing popularity of indoor location-based services (LBS) in practice and research. Among others, indoor shortest path query (ISPQ) is of fundamental importance for indoor LBS. However, existing works on ISPQ ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a doors accessibility synchronously and asynchronously. Furthermore, we propose a novel index structure (IT-Index) that extends the state-of-the-art index significantly by storing dynamic door-to-door distances in a compact distance cube associated with tree nodes. When processing ITSPQ using IT-Index, we make use of the distance cube to avoid time-consuming indoor distance computation on-the-fly. We evaluate the proposed techniques using extensive experiments on synthetic and real data. The results show that our IT-Index based method is the most efficient for processing ITSPQ at a modest cost of index memory consumption.

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object$o$, e.g., a person confirmed to be a virus carrier, anICQanalyzes uncertain indoor positioning data to find objects that most likely had close contact with$o$for a long period of time. To processICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary forICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals.

Trip itinerary recommendation finds an ordered sequence of Points-of-Interest (POIs) from a large number of candidate POIs in a city. In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-$k$alternative itineraries for given source and destination POIs. These alternative itineraries would be not only popular given the historical routes adopted by past users but also dissimilar (or diverse) to each other. The DeepAltTrip consists of two major components: (i)Itinerary Net(ITRNet) which estimates the likelihood of POIs on an itinerary by using graph autoencoders and two (forward and backward) LSTMs; and (ii) a route generation procedure to generate$k$diverse itineraries passing through relevant POIs obtained using ITRNet. For the route generation step, we propose a novel sampling algorithm that can seamlessly handle a wide variety of user-defined constraints. To the best of our knowledge, this is the first work thatlearnsfrom historical trips to provide a set of alternative itineraries to the users. Extensive experiments conducted on eight popular real-world datasets show the effectiveness and efficacy of our approach over state-of-the-art methods.

Abstract Alternative pathfinding requires finding a set of k alternative paths (including the shortest path) between a given source s and a target t. Intuitively, these paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While finding alternative paths in road networks has been extensively studied, to the best of our knowledge, we are the first to formally study alternative pathfinding in game maps which are typically represented as Euclidean planes containing polygonal obstacles. First, we adapt the existing techniques designed for road networks to find alternative paths in the game maps. Then, based on our web-based system that visualises alternative paths generated by different approaches, we conduct a user study that shows that the existing road network approaches generate high-quality alternative paths when adapted for the game maps. However, these existing approaches are computationally inefficient especially when compared to the state-of-the-art shortest path algorithms. Motivated by this, we propose novel data structures and exploit these to develop an efficient algorithm to compute high-quality alternative paths. that shows that the existing road network approaches generate high-quality alternative paths in game maps. Our extensive experimental study demonstrates that our proposed algorithm is more than an order of magnitude faster than the existing approaches and returns alternative paths of comparable quality. Furthermore, our algorithm is comparable to a state-of-the-art shortest path algorithm in terms of running time.

In this paper, we present energy-aware scheduling for Serverless edge computing. Energy awareness is critical since edge nodes, in many Internet of Things (IoT) domains, are meant to be powered by renewable energy sources that are variable, making low-powered and/or overloaded (bottleneck) nodes unavailable and not operating their services. This awareness is also required since energy challenges have not been previously addressed by Serverless, largely due to its origin in cloud computing. To achieve this, we formally model an energy-aware resource scheduling problem in Serverless edge computing, given a cluster of battery-operated and renewable-energy powered nodes. Then, we devise zone-oriented and priority-based algorithms to improve the operational availability of bottleneck nodes. As assets, our algorithm coins terms “sticky offloading” and “warm scheduling” in the interest of the Quality of Service (QoS). We evaluate our proposal against well-known benchmarks using real-world implementations on a cluster of Raspberry Pis enabled with container orchestration, Kubernetes, and Serverless computing, OpenFaaS, where edge nodes are powered by real-world solar irradiation. Experimental results achieve significant improvements, up to 33%, in helping bottleneck node's operational availability while preserving the QoS. With energy awareness, now Serverless can unconditionally offer its resource efficiency and portability at the edge.

Computing multiple alternative routes from a source$s$to a target$t$has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen comparing four of the most popular existing approaches including Google Maps. We report the average ratings received by the four approaches, and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers interpret these results with caution.

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.

Many modern navigation systems and map-based services do not only provide the fastest route from a source location$s$to a target location$t$but also provide a few alternative routes to the users as more options to choose from. Consequently, computing alternative paths has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps. We also present a web-based demo system that can be accessed using any internet-enabled device and allows users to see the alternative routes generated by the four approaches for any pair of selected source and target. We report the average ratings received by the four approaches and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers to interpret these results with caution because certain factors may have affected the participants’ ratings.

To support indoor spatial queries and indoor location-based services (LBS), multiple techniques including model/indexes and search algorithms have been proposed. In this work, we conduct an extensive experimental study on existing proposals for indoor spatial queries. We survey five model/indexes, compare their algorithmic characteristics, and analyze their space and time complexities. We also design an in-depth benchmark with real and synthetic datasets, evaluation tasks and performance metrics. Enabled by the benchmark, we obtain and report the performance results of all model/indexes under investigation. By analyzing the results, we summarize the pros and cons of all techniques and suggest the best choice for typical scenarios.

A k nearest neighbors (kNN) query finds k closest points-of-interest (POIs) from an agent's location. In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query. An AkNN query retrieves k POIs with the smallest aggregate distances where the aggregate distance of a POI is obtained by aggregating its distances from the multiple agents (e.g., sum of its distances from each agent). We propose a novel data structure COLT (Compacted Object-Landmark Tree) which enables efficient hierarchical graph traversal and utilize it to efficiently answer AkNN queries. Our experiments on real-world and synthetic data sets show that our techniques outperform existing approaches by more than an order of magnitude in almost all settings.

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this demonstration, we present IndoorViz, a new indoor spatial data management system that integrates three novel index structures proposed in [4] and [6] with well designed query processing algorithms and 3D visualization functions. The IndoorViz is able to support indoor spatial object indexing, efficient query processing and interactive 3D display.

The shortest path problem (SPP) asks us to find a minimum length path between two points, usually on a graph. In a Euclidean environment the points are in a 2D plane and here the path must avoid a set of polygonal obstacles. Solution methods for this Euclidean SPP (ESPP) typically convert the continuous 2D map into a discretised representation, like a graph or navigation mesh. RayScan is a recent and fast ESPP algorithm which avoids the preprocessing step by using a combination of "ray shooting" and polygon scanning. In this paper we improve the performance of RayScan using spatial reasoning and ray caching techniques. We also extend the algorithm, from single-target search to a multi-target setting. Comparative game map experiments show a substantial speedup.

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we first propose two novel indexes called Indoor Partitioning Tree (IP-Tree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide near-optimal performance for shortest distance and shortest path queries. We are also the first to study spatial keyword queries in indoor venues. We propose a novel data structure called Keyword Partitioning Tree (KP-Tree) that indexes objects in an indoor partition. We propose an efficient algorithm based on VIP-Tree and KP-Trees to efficiently answer spatial keyword queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing solutions by several orders of magnitude.

Indoor mobility semantics analytics can greatly benefit many pertinent applications. Existing semantic annotation methods mainly focus on outdoor space and require extra knowledge such as POI category or human activity regularity. However, these conditions are difficult to meet in indoor venues with relatively small extents but complex topology. This work studies the annotation of indoor mobility semantics that describe an object's mobility event (what ) at a semantic indoor region (where ) during a time period (when ). A coupled conditional Markov network (C2MN) is proposed with a set of feature functions carefully designed by incorporating indoor topology and mobility behaviors. C2MN is able to capture probabilistic dependencies among positioning records, semantic regions, and mobility events jointly. Nevertheless, the correlation of regions and events hinders the parameters learning. Therefore, we devise an alternate learning algorithm to enable the parameter learning over correlated variables. The extensive experiments demonstrate that our C2MN-based semantic annotation is efficient and effective on both real and synthetic indoor mobility data.

Given the prevalence and volume of local search queries, today's search engines are required to find results by both spatial proximity and textual relevance at high query throughput. Existing techniques to answer such spatial keyword queries employ a keyword aggregation strategy that suffers from certain drawbacks when applied to road networks. Instead, we propose the K-SPIN framework, which uses an alternative keyword separation strategy that is more suitable on road networks. While this strategy was previously thought to entail prohibitive pre-processing costs, we further propose novel techniques to make our framework viable and even light-weight. Thorough experimentation shows that K-SPIN outperforms the state-of-the-art by up to two orders of magnitude on a wide range of settings and real-world datasets.

Indoor shortest path query (ISPQ) is of fundamental importance for indoor location-based services (LBS). However, existing ISPQs ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a door's accessibility synchronously and asynchronously, respectively. We experimentally evaluate the proposed techniques using synthetic data. The results show that our methods are efficient.

We consider optimal and anytime algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions. Our approach leverages ideas from two recent works: Polyanya, a mesh-based ESPP planner which we use to represent and reason about the environment, and Compressed Path Databases, a speedup technique for pathfinding on grids and spatial networks, which we exploit to compute fast candidate paths. In a range of experiments and empirical comparisons we show that: (i) the auxiliary data structures required by the new method are cheap to build and store; (ii) for optimal search, the new algorithm is faster than a range of recent ESPP planners, with speedups ranging from several factors to over one order of magnitude; (iii) for anytime search, where feasible solutions are needed fast, we report even better runtimes.

A significant proportion of all search volume consists of local searches. As a result, search engines must be capable of finding relevant results combining both spatial proximity and textual relevance with high query throughput. We observe that existing techniques answering these spatial keyword queries use keyword aggregated indexing, which has several disadvantages on road networks. We propose K-SPIN, a versatile framework that instead uses keyword separated indexing to delay and avoid expensive operations. At first glance, this strategy appears to have impractical pre-processing costs. However, by exploiting several useful observations, we make the indexing cost not only viable but also light-weight. For example, we propose a novel p-Approximate Network Voronoi Diagram (NVD) with one order of magnitude less space cost than exact NVDs. By carefully exploiting features of the K-SPIN framework, our query algorithms are up to two orders of magnitude more efficient than the state-of-the-art as shown in our experimental investigation on various queries, parameter settings, and real road network and keyword datasets.

Given a set of obstacles in 2D or 3D space, a set of n candidate locations where facilities can be established, the Maximum Visibility Facility Selection (MVFS) query finds k out of the n locations, that yield the maximum visibility coverage of the data space. Though the MVFS problem has been extensively studied in visual sensor networks, computational geometry, and computer vision in the form of optimal camera placement problem, existing solutions are designed for discretized space and only work for MVFS instances having a few hundred facilities. In this paper, we revisit the MVFS problem to support new spatial database applications like "where to place security cameras to ensure better surveillance of a building complex?" or "where to place billboards in the city to maximize visibility from the surrounding space?". We introduce the concept of equivisibility triangulation to devise the first approach to accurately determine the visibility coverage of continuous data space from a subset of the facility locations, which avoids the limitations of discretizing the data space. Then, we propose an efficient graph-theoretic approach that exploits the idea of vertex separators for efficient exact in-memory solution of the MVFS problem. Finally, we propose the first external-memory based approximation algorithm (with a guaranteed approximation ratio of 1 - 1/e) that is scalable for a large number of obstacles and facility locations. We conduct extensive experimental study to show the effectiveness and efficiency of our proposed algorithms.

In this paper, we study continuous detour queries in the indoor space. A continuous detour query finds the nearest indoor detour object like an ATM or a printer for a moving user walking towards a target location in an indoor venue, where the detour distance for an indoor object is measured as the total indoor distance of the object from the user's current and target locations. The continuous detour query has been already studied for the outdoor space, but the solutions are not adaptable for the indoor space due to the unique characteristics of indoor venues. We develop the first solution for efficient processing of the continuous detour query in the indoor space. The novelty of our solution comes from the computation of safe zones for the indoor objects by exploiting the geometric properties of hyperbolas, additively weighted Voronoi diagram and indoor partitions. The safe zone represents an area such that the nearest detour object remains unchanged as long as the user is in this area. The key ideas behind the efficiency of our solution are reducing the number of re-evaluation of the detour queries for the location change of a moving user, pre-computing the safe zones, and indexing them using a grid structure. The experiments show that our solution can process continuous detour queries efficiently and reduces the communication overhead.

A route planning query has many real-world applications and has been studied extensively in outdoor spaces such as road networks or Euclidean space. Despite its many applications in indoor venues (e.g., shopping centres, airports), almost all existing studies are specifically designed for outdoor spaces and do not take into account unique properties of the indoor spaces such as hallways, stairs, escalators, rooms etc. We identify this research gap and formally define the problem of category aware multi-criteria route planning query, denoted by CAM, which returns the optimal route from an indoor source point to an indoor target point that passes through at least one indoor point from each given category while minimizing the total cost of the route in terms of travel distance and other relevant attributes. We show that CAM query is NP-hard. We propose an efficient approximation algorithm which generates high-quality results. We provide an extensive experimental study conducted on the largest shopping centre in Australia and compare our algorithms with alternative approaches. The experiments demonstrate that our algorithm is highly efficient and produces quality results.

In the age of smartphones, finding the nearest points of interest (POIs) is a highly relevant problem. A popular way to solve this is to use a k Nearest Neighbor (kNN) query to retrieve POIs by their road network distances from a query location. However, we find that existing kNN methods have not been carefully compared. We present a detailed and fair experimental study of the state-of-the-art, documenting the many insights gleaned along the way. Notably, a long overlooked Euclidean distance heuristic is often the best performing method by a wide margin. We have also released all code as open-source for readers to reproduce experiments and easily add methods or queries to the testbed for new studies.

In this paper, we study a new type of indoor queries, called the indoor trip planning query (iTPQ). We have observed that the existing methods for outdoor spaces cannot be applied directly to indoor spaces, due to the difference in the underlying networks. Outdoor spaces, which are normally represented as spatial road networks, are commonly modelled as a graph. In contrast, indoor spaces have distinct features (e.g. rooms, doors, hallways) that do not exist in road networks. So far, no specific solutions have been proposed for iTPQ. Even if outdoor techniques are revised for iTPQ, they fail to process iTPQ efficiently. In this paper, we propose an indoor-specific technique, based on the indoor VIP-Tree, called the VIP-Tree neighbor expansion (VNE) method, that also includes new pruning techniques in both pre-processing and query processing phases. Our experimental results show that our proposed method VNE outperforms other indoor and outdoor algorithms by several orders of magnitude in terms of processing time with low indexing cost.

The widespread proliferation of location-acquisition techniques and GPS-embedded mobile devices have resulted in the generation of geo-tagged data at unprecedented scale and have essentially enhanced the user experience in location-based services associated with social networks. Such location-based social networks allow people to record and share their location and are a rich source of information which can be exploited to study people’s various attributes and characteristics to provide various Geo-Social (GS) services. In this paper, we propose two new types of queries called Top-k famous placesTkFP and Socio-Spatial Skyline QuerySSSQ query, which enrich the semantics of the conventional spatial queries by introducing a social relevance component. In addition, three approaches namely, (1) Social-First, (2) Spatial-First and (3) Hybrid are proposed to efficiently process TkFP and SSSQ queries. Finally, we conduct an extensive evaluation of the proposed schemes using real and synthetic datasets and demonstrate the effectiveness of the proposed approaches.

Given a set of facilities and a set of users, a reverse nearest neighbors (RNN) query retrieves every user$u$for which the query facility$q$is its closest facility. Since$q$is the closest facility to$u$, the user$u$is said to be influenced by$q$. In this paper, we propose arelaxeddefinition of influence where a user$u$is said to be influenced by not only its closest facility but also every other facility that isalmostas close to$u$as its closest facility is. Based on this definition of influence, we propose reverse approximate nearest neighbors (RANN) queries. Formally, given a value$x>1$, an RANN query$q$returns every user$u$for which$dist(u,q) \leq x\times NNDist(u)$where$NNDist(u)$denotes the distance between a user$u$and its nearest facility, i.e.,$q$is an approximate nearest neighbor of$u$. In this paper, we study bothsnapshotandcontinuousversions of RANN queries. In a snapshot RANN query, the underlying data sets do not change and the results of a query are to be computed only once. In the continuous version, the users continuously change their locations and the results of RANN queries are to be continuously monitored. Based on effective pruning techniques and several non-trivial observations, we propose efficient RANN query processing algorithms for both the snapshot and continuous RANN queries. We conduct extensive experiments on both real and synthetic data sets and demonstrate that our algorithm for both snapshot and continuous queries are significantly better than the competitors.

The rise of innovative transportation services and the recent breakthrough in the development of autonomous vehicles have stimulated the research on collective travel planning problems such as ride-sharing, carpooling, and on-demand vehicle routing in recent years. In this paper, we introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively. The goal of each problem is to minimize the aggregate cost of the individual travelers' paths and the shared route under various constraints. First, we introduce the optimal end-stops (OES) query that finds a pair of pick-up-and-drop-off locations such that the sum of the distance between these locations and the total distance traveled by the travelers from their start locations to the pick-up location and from the drop-off location to their end locations is minimized. We propose a polynomial-time fast algorithm for the OES query by utilizing the path-coherence property of road networks. Second, we formulate the optimal route and intermediate stops (ORIS) query to find a set of intermediate stops for the vehicle such that the sum of the total distance traveled by the vehicle and the total distance traveled by the travelers from their start locations to one of the stops and to their end locations from one of the stops is minimized. We propose a novel near-optimal polynomial-time-and-space heuristic algorithm for the ORIS query that performs reasonably well in practice. We also analyze several variants of this problem. Finally, we perform extensive experiments to demonstrate the efficiency and efficacy of our algorithms.

In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues. Some recent initiatives have started to cross this technical barrier, promising huge future opportunities for research organizations, government agencies, technology giants, and enterprizing start-ups - to exploit the potential of indoor LBS. Consequently, indoor data management has gained significant research attention in the past few years and the research interest is expected to surge in the upcoming years. This will result in a broad range of indoor applications including emergency services, public services, in-store advertising, shopping, tracking, guided tours, and much more. In this tutorial, we first highlight the importance of indoor data management and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing research in indoor data management, covering modeling, cleansing, indexing, querying, and other relevant topics. Finally, we discuss the future research directions in this important and growing research area, discussing spatial-textual search, integrating outdoor and indoor spaces, uncertain indoor data, and indoor trajectory mining.

Given a set of facilities F, a set of users U and a query facility q, a reverse k furthest neighbors (RkFN) query retrieves every user u ∈ U for which q is one of its k-furthest facilities. RkFN query is the natural complement of reverse k-nearest neighbors (RkNN) query that returns every user u for which q is one of its k-nearest facilities. While RkNN query returns the users that are highly influenced by a query q, RkFN query aims at finding the users that are least influenced by a query q. RkFN query has many applications in location-based services, marketing, facility location, clustering, and recommendation systems etc. While there exist several algorithms that answer RkFN query for k = 1, we are the first to propose a solution for arbitrary value of k. Based on several interesting observations, we present an efficient algorithm to process the RkFN queries. We also present a rigorous theoretical analysis to study various important aspects of the problem and our algorithm. An extensive experimental study is conducted using both real and synthetic data sets, demonstrating that our algorithm outperforms the state-of-the-art algorithm even for k = 1. The accuracy of our theoretical analysis is also verified by the experiments.

Given a set of objects and a query q, a point p is q’s Reverse k Nearest Neighbour (RkNN) if q is one of p’s k-closest objects. RkNN queries have received significant research attention in the past few years. However, we realize that the state-of-the-art algorithm, SLICE, accesses many objects that do not contribute to its RkNN results when running the filtering phase, which deteriorates the query performance. In this paper, we propose a novel RkNN algorithm with pre-computation by partitioning the data space into disjoint rectangular regions and constructing the guardian set for each region R. We guarantee that, for each q that lies in R, its RkNN results are only affected by the objects in R’s guardian set. The advantage of this approach is that the results of a query $$q\in R$$ can be computed by using SLICE on only the objects in its guardian set instead of using the whole dataset. Besides, we raise two new useful variants of RkNN and propose algorithms. Our comprehensive experimental study on synthetic and real the proposed approaches are the most efficient algorithms for RkNN and its variants.

A k nearest neighbor ( k NN) query on road networks retrieves the k closest points of interest (POIs) by their network distances from a given location. Today, in the era of ubiquitous mobile computing, this is a highly pertinent query. While Euclidean distance has been used as a heuristic to search for the closest POIs by their road network distance, its efficacy has not been thoroughly investigated. The most recent methods have shown significant improvement in query performance. Earlier studies, which proposed disk-based indexes, were compared to the current state-of-the-art in main memory. However, recent studies have shown that main memory comparisons can be challenging and require careful adaptation. This paper presents an extensive experimental investigation in main memory to settle these and several other issues. We use efficient and fair memory-resident implementations of each method to reproduce past experiments and conduct additional comparisons for several overlooked evaluations. Notably we revisit a previously discarded technique (IER) showing that, through a simple improvement, it is often the best performing technique.

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we propose two novel indexes called Indoor Partitioning Tree (IP-Tree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide near-optimal performance for shortest distance and shortest path queries. We also present efficient algorithms for other spatial queries such as k nearest neighbors queries and range queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing algorithms by several orders of magnitude.

The k-nearest neighbor approach (k-NN) has been extensively used as a powerful non-parametric technique in many scientific and engineering applications. However, this approach incurs a large computational cost. Hence, this issue has become an active research field. In this work, a novel k-NN approach based on various-widths clustering, named kNNVWC, to efficiently find k-NNs for a query object from a given data set, is presented. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. This reduces the clustering time, in addition to balancing the number of produced clusters and their respective sizes. Maximum efficiency is achieved by using triangle inequality to prune unlikely clusters. Experimental results demonstrate that kNNVWC performs well in finding k-NNs for query objects compared to a number of k-NN search algorithms, especially for a data set with high dimensions, various distributions and large size.

In many domains such as computational geometry and database management, an object may be described by multiple instances (points). Then the distance (or similarity) between two objects is captured by the pair-wise distances among their instances. In the past, numerous nearest neighbor (NN) functions have been proposed to define the distance between objects with multiple instances and to identify the NN object. Nevertheless, considering that a user may not have a specific NN function in mind, it is desirable to provide her with a set of NN candidates. Ideally, the set of NN candidates must include every object that is NN for at least one of the NN functions and must exclude every non-promising object. However, no one has studied the problem of NN candidates computation from this perspective. Although some of the existing works aim at returning a set of candidate objects, they do not focus on the NN functions while computing the candidate objects. As a result, they either fail to include an NN object w.r.t. some NN functions or include a large number of unnecessary objects that have no potential to be the NN regardless of the NN functions.

Given a set of clients C, a set of facilities F and a query q∈F, a reverse k-nearest neighbor (RkNN) query retrieves every client c∈C for which q is one of the k closest facilities. In the past few years, RkNN queries have received significant research attention due to their wide range of applications. In this paper, we study the problem of continuous monitoring of RkNN queries in road networks. The state-of-the-art technique is sensitive toward the movement of clients, e.g. whenever a client that is inside the so-called unpruned region changes its location, the existing technique requires expensive verification of whether the client is an RkNN of q or not. To address this problem, we utilize the novel concept of influence zone, which is a region in the network such that a client c is the RkNN if and only if it lies inside this zone. This significantly improves performance because the problem of continuously monitoring RkNN queries is reduced to the problem of continuously monitoring the clients that are inside this zone. Based on several non-trivial observations, we present an efficient algorithm to compute the influence zone. Our extensive experimental study demonstrates that our algorithm is more than an order of magnitude faster than the state-of-the-art algorithm.

A facility |$f$| is said to influence a user |$u$| if |$f$| is one of the |$k$| closest facilities of the user |$u$|⁠. This is because users usually prefer to visit/use nearby facilities. The influence set of a facility |$f$| is the set of users influenced by |$f$|⁠. The computation of the influence set has gained extensive research attention in the past decade. Note that the definition of influence set assumes that each user behaves the same, i.e. each user prefers the |$k$| closest facilities where |$k$| is a constant. However, in real-world scenarios, different users may prefer different values of |$k$| depending on, for example, the density of facilities around them, the mode of transport available to them etc. Therefore, assuming a constant value of |$k$| for each user may not be able to capture the essence of influence effectively. In this paper, we compute the influence of a facility |$f$| using the query logs that contain the |$k$|-nearest neighbors queries issued in the past and essentially represent the preferred value of |$k$| for each user. Specifically, a facility |$f$| has an impact on a user |$u$| if |$f$| is one of the facilities in the answer set of the query issued by the user. The set of such users is called impact set to avoid ambiguity with influence set. To the best of our knowledge, we are the first to study the problem of impact set computation using query logs. Although existing techniques can be extended to compute the impact set, these techniques suffer from several serious limitations. We carefully analyze these limitations and propose an algorithm to answer impact set queries for 2D location data. The proposed algorithm uses a novel access order and several non-trivial observations to address these limitations. We conduct an extensive experimental study on real and synthetic data sets and demonstrate that our algorithm significantly outperforms existing algorithms in terms of both CPU cost and I/O cost.

Given a set of users, a set of facilities and a query facility q , a reverse k nearest neighbors (R k NN) query returns every user u for which the query is one of its k closest facilities. R k NN queries have been extensively studied under a variety of settings and many sophisticated algorithms have been proposed to answer these queries. However, the existing experimental studies suffer from a few limitations. For example, some studies estimate the I/O cost by charging a fixed penalty per I/O and we show that this may be misleading. Also, the existing studies either use an extremely small buffer or no buffer at all which puts some algorithms at serious disadvantage. We show that the performance of these algorithms is significantly improved even when a small buffer (containing 100 pages) is used. Finally, in each of the existing studies, the proposed algorithm is mainly compared only with its predecessor assuming that it was the best algorithm at the time which is not necessarily true as shown in our experimental study. Motivated by these limitations, we present a comprehensive experimental study that addresses these limitations and compares some of the most notable algorithms under a wide variety of settings. Furthermore, we also present a carefully developed filtering strategy that significantly improves TPL which is one of the most popular R k NN algorithms. Specifically, the optimized version is up to 20 times faster than the original version and reduces its I/O cost up to two times.

The computation of k-lower envelope is a classical problem and has been very well studied for main memory non-indexed data. In this paper, we study the problem from the database perspective and present the first algorithm which utilizes the presence of the index and achieves access optimality, i.e., it accesses a node of the index only if the correctness of the results cannot be guaranteed without accessing this node. We also demonstrate the applications of k-lower envelope in ranking systems. Let an object be called valuable if it is one of the top-k objects according to at least one linear scoring function. In this paper, we answer the following important questions that may be asked by different users: 1) I am not sure what scoring function I should use, therefore, return me the set of valuable objects so that I can select an object I like the most; 2) How can I modify the attributes (e.g., price) of my product such that it becomes a valuable object; 3) What are the preference functions for which a given object is among the top-k objects. These three questions are formalized and called k-snippet, k-depth contour and reverse top-k query, respectively. We propose a unified framework to solve these queries by utilizing k-lower envelope as a common foundation. Our main algorithm is access optimal for k-snippet and k-lower envelope computation. We also demonstrate its access optimality for the k-depth contour problem when k is smaller than the minimum number of objects in any leaf node of the index structure. Our algorithms outperform state-of-the-art algorithms by more than an order of magnitude in terms of both CPU and I/O cost.

With the increasing pervasiveness of the geo-positioning tech-nologies, there is an enormous amount of spatio-textual ob-jects available in many applications such as location based services and social networks. Consequently, various types of spatial keyword searches which explore both locations and textual descriptions of the objects have been intensively studied by the research communities and commercial orga-nizations. In many important applications (e.g., location based services), the closeness of two spatial objects is mea-sured by the road network distance. Moreover, the result diversification is becoming a common practice to enhance the quality of the search results. Motived by the above facts, in this paper we study the problem of diversified spa-tial keyword search on road networks which considers both the relevance and the spatial diversity of the results. An efficient signature-based inverted indexing technique is pro-posed to facilitate the spatial keyword query processing on road networks. Then we develop an efficient diversified spa-tial keyword search algorithm by taking advantage of spatial keyword pruning and diversity pruning techniques. Com-prehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods. 1.

Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the k-closest facilities. Due to its importance, RkNN query has received significant research attention in the past few years. Almost all of the existing techniques adopt a pruning-and-verification framework. Regions-based pruning and half-space pruning are the two most notable pruning strategies. The half-space based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the half-space pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regions-based pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new RkNN algorithm called SLICE that utilizes the strength of regions-based pruning and overcomes its limitations. Our extensive experimental study on synthetic and real data sets demonstrate that SLICE is significantly more efficient than the existing algorithms. We also provide a detailed theoretical analysis to analyze various aspects of our algorithm such as I/O cost, the unpruned area, and the cost of its verification phase etc. The experimental study validates our theoretical analysis.

The dominance operator plays an important role in a wide spectrum of multi-criteria decision making applications. Generally speaking, a dominance operator is a partial order on a set O of objects, and we say the dominance operator has the monotonic property regarding a family of ranking functions F if o1 dominates o2 implies f(o1) ≥ f(o2) for any ranking function f ∈ F and objects o1, o2 ∈ O. The dominance operator on the multi-dimensional points is well defined, which has the monotonic property regarding any monotonic ranking (scoring) function. Due to the uncertain nature of data in many emerging applications, a variety of existing works have studied the semantics of ranking query on uncertain objects. However, the problem of dominance operator against multi-dimensional uncertain objects remains open. Although there are several attempts to propose dominance operator on multi-dimensional uncertain objects, none of them claims the monotonic property on these ranking approaches.

Given a scoring function that computes the score of a pair of objects, a top-$k$pairs query returns$k$pairs with the smallest scores. In this paper, we present a unified framework for answering generic top-$k$pairs queries including$k$-closest pairs queries,$k$-furthest pairs queries and their variants. Note that$k$-closest pairs query is a special case of top-$k$pairs queries where the scoring function is the distance between the two objects in a pair. We are the first to present a unified framework to efficiently answer a broad class of top-$k$queries including the queries mentioned above. We present efficient algorithms and provide a detailed theoretical analysis that demonstrates that the expected performance of our proposed algorithms is optimal for two dimensional data sets. Furthermore, our framework does not require pre-built indexes, uses limited main memory and is easy to implement. We also extend our techniques to support top-$k$pairs queries on multi-valued (or uncertain) objects. We also demonstrate that our framework can handle exclusive top-$k$pairs queries. Our extensive experimental study demonstrates effectiveness and efficiency of our proposed techniques.

Top-k pairs and top-k objects queries have received significant attention by the research community. In this paper, we present the first approach to answer a broad class of top-k pairs and top-k objects queries over sliding windows. Our framework handles multiple top-k queries and each query is allowed to use a different scoring function, a different value of k, and a different size of the sliding window. Furthermore, the framework allows the users to define arbitrarily complex scoring functions and supports out-of-order data streams. For all the queries that use the same scoring function, we need to maintain only one K-skyband. We present efficient techniques for the K-skyband maintenance and query answering. We conduct a detailed complexity analysis and show that the expected cost of our approach is reasonably close to the lower bound cost. For top-k pairs queries, we demonstrate the efficiency of our approach by comparing it with a specially designed supreme algorithm that assumes the existence of an oracle and meets the lower bound cost. For top-k objects queries, our experimental results demonstrate the superiority of our algorithm over the state-of-the-art algorithm.

Given a set of criterions, an object o dominates another object ó if o is more preferable than ó according to every criterion. A skyline query returns every object that is not dominated by any other object. In this paper, we study the problem of continuously monitoring a moving skyline query where one of the criterions is the distance between the objects and the moving query. We propose a safe zone based approach to address the challenge of efficiently updating the results as the query moves. A safe zone is the area such that the results of a query remain unchanged as long as the query lies inside this area. Hence, the results are required to be updated only when the query leaves its safe zone. Although the main focus of this paper is to present the techniques for Euclidean distance metric, the proposed techniques are applicable to any metric distance (e.g., Manhattan distance, road network distance). We present several non-trivial optimizations and propose an efficient algorithm for safe zone construction. Our experiments demonstrate that the cost of our safe zone based approach is reasonably close to a lower bound cost and is three orders of magnitude lower than the cost of a naïve algorithm.

A traditional query returns a set of objects that satisfy user defined criteria at the time query was issued. The results are based on the values of objects at query time and may be affected by outliers. Intuitively, an object better meets the user's needs if it persistently satisfies the criteria, i.e., it satisfies the criteria for majority of the time in the past T time units. In this paper, we propose a measure named loyalty that reflects how persistently an object satisfies the criteria. Formally, the loyalty of an object is the total time (in past T time units) it satisfies the query criteria. In this paper, we study top-k loyalty queries over sliding windows that continuously report k objects with the highest loyalties. Each object issues an update when it starts satisfying the criteria or when it stops satisfying the criteria. We show that the lower bound cost of updating the results of a top-k loyalty query is O(logN), for each object update, where N is the number of updates issued in last T time units. We conduct a detailed complexity analysis and show that our proposed algorithm is optimal. Moreover, effective pruning techniques are proposed to improve the efficiency. We experimentally verify the effectiveness of the proposed approach by comparing it with a classic sweep line algorithm.

Top-k pairs queries have received significant attention by the research community. k-closest pairs queries, k-furthest pairs queries and their variants are among the most well studied special cases of the top-k pairs queries. In this paper, we present the first approach to answer a broad class of top-k pairs queries over sliding windows. Our framework handles multiple top-k pairs queries and each query is allowed to use a different scoring function, a different value of k and a different size of the sliding window. Although the number of possible pairs in the sliding window is quadratic to the number of objects N in the sliding window, we efficiently answer the top-k pairs query by maintaining a small subset of pairs called K-sky band which is expected to consist of O(K log(N/K)) pairs. For all the queries that use the same scoring function, we need to maintain only one K-sky band. We present efficient techniques for the K-sky band maintenance and query answering. We conduct a detailed complexity analysis and show that the expected cost of our approach is reasonably close to the lower bound cost. We experimentally verify this by comparing our approach with a specially designed supreme algorithm that assumes the existence of an oracle and meets the lower bound cost.

In many applications involving multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions for selecting one object that fits best their personal needs. As a key feature, the skyline in a multidimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no matter how their preferences vary. Driven by many recent applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this article we propose a novel skyline operator, namely stochastic skylines. In the light of the expected utility principle, stochastic skylines guarantee to provide the minimum set of candidates to optimal solutions over a family of utility functions. We first propose the lskyline operator based on the lower orthant orders . lskyline guarantees to provide the minimum set of candidates to the optimal solutions for the family of monotonic multiplicative utility functions. While lskyline works very effectively for the family of multiplicative functions, it may miss optimal solutions for other utility /scoring functions (e.g., linear functions). To resolve this, we also propose a general stochastic skyline operator, gskyline , based on the usual orders . gskyline provides the minimum candidate set to the optimal solutions for all monotonic functions. For the first time regarding the existing literature, we investigate the complexities of determining a stochastic order between two uncertain objects whose probability distributions are described discretely . We firstly show that determining the lower orthant order is NP-complete with respect to the dimensionality; consequently the problem of computing lskyline is NP-complete. We also show an interesting result as follows. While the usual order involves more complicated geometric forms than the lower orthant order, the usual order may be determined in polynomial time regarding all the inputs, including the dimensionality; this implies that gskyline can be computed in polynomial time. A general framework is developed for efficiently and effectively retrieving lskyline and gskyline from a set of uncertain objects, respectively, together with efficient and effective filtering techniques. Novel and efficient verification algorithms are developed to efficiently compute lskyline over multidimensional uncertain data, which run in polynomial time if the dimensionality is fixed, and to efficiently compute gskyline in polynomial time regarding all inputs. We also show, by theoretical analysis and experiments, that the sizes of lskyline and gskyline are both quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs.

Top-k pairs queries have many real applications. k closest pairs queries, k furthest pairs queries and their bichromatic variants are some of the examples of the top-k pairs queries that rank the pairs on distance functions. While these queries have received significant research attention, there does not exist a unified approach that can efficiently answer all these queries. Moreover, there is no existing work that supports top-k pairs queries based on generic scoring functions. In this paper, we present a unified approach that supports a broad class of top-k pairs queries including the queries mentioned above. Our proposed approach allows the users to define a local scoring function for each attribute involved in the query and a global scoring function that computes the final score of each pair by combining its scores on different attributes. We propose efficient internal and external memory algorithms and our theoretical analysis shows that the expected performance of the algorithms is optimal when two or less attributes are involved. Our approach does not require any pre-built indexes, is easy to implement and has low memory requirement. We conduct extensive experiments to demonstrate the efficiency of our proposed approach.

Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone which is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location based services, marketing and decision support systems. It can also be used to efficiently process RkNN queries. First, we present efficient algorithm to compute the influence zone. Then, based on the influence zone, we present efficient algorithms to process RkNN queries that significantly outperform existing best known techniques for both the snapshot and continuous RkNN queries. We also present a detailed theoretical analysis to analyse the area of the influence zone and IO costs of our RkNN processing algorithms. Our experiments demonstrate the accuracy of our theoretical analysis.

In many applications involving the multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions. As a key feature, the skyline in a multi-dimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no mater how their preferences vary. Driven by many applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this paper we propose a novel skyline operator, namely stochastic skyline. In the light of the expected utility principle, stochastic skyline guarantees to provide the minimum set of candidates for the optimal solutions over all possible monotonic multiplicative utility functions. In contrast to the conventional skyline or the probabilistic skyline computation, we show that the problem of stochastic skyline is NP-complete with respect to the dimensionality. Novel and efficient algorithms are developed to efficiently compute stochastic skyline over multi-dimensional uncertain data, which run in polynomial time if the dimensionality is fixed. We also show, by theoretical analysis and experiments, that the size of stochastic skyline is quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs.

Given a positive value r, a distance-based range query returns the objects that lie within the distance r of the query location. In this paper, we focus on the distance-based range queries that continuously change their locations in a euclidean space. We present an efficient and effective monitoring technique based on the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be reevaluated unless it leaves the safe zone. Our contributions are as follows: 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) We theoretically determine and experimentally verify the expected distance a query moves before leaving the safe zone and, for majority of queries, the expected number of guard objects. 3) Our experiments demonstrate that the proposed approach is close to optimal and is an order of magnitude faster than a naïve algorithm. 4) We also extend our technique to monitor the queries in a road network. Our algorithm is up to two order of magnitude faster than a naïve algorithm.

Given a positive value r, a circular range query returns the objects that lie within the distance r of the query location. In this paper, we study the circular range queries that continuously change their locations. We present an efficient and effective technique to monitor such moving range queries by utilising the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be re-evaluated unless it leaves the safe zone. The shape of the safe zone is defined by the so-called guard objects. The cost of checking whether a query lies in the safe zone takes k distance computations, where k is the number of the guard objects. Our contributions are as follows. 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) To show the effectiveness of the safe zone, we theoretically evaluate the probability that a query leaves the safe zone within one time unit and the expected distance a query moves before it leaves the safe zone. Additionally, for the queries that have diameter of the safe zone less than its expected value multiplied by a constant, we also give an upper bound on the expected number of guard objects. This upper bound turns out to be a constant, that is, it does not depend either on the radius r of the query or the density of the objects. The theoretical analysis is verified by extensive experiments. 3) Our thorough experimental study demonstrates that our proposed approach is close to optimal and is an order of magnitude faster than a nai¿ve algorithm.

K Nearest Neighbor search has many applications including data mining, multi-media, image processing, and monitoring moving objects. In this paper, we study the problem of KNN over multi-valued objects. We aim to provide effective and efficient techniques to identify KNN sensitive to relative distributions of objects.We propose to use quantiles to summarize relative-distribution-sensitive K nearest neighbors. Given a query Q and a quantile ¿ ¿ (0, 1), we firstly study the problem of efficiently computing K nearest objects based on a ¿-quantile distance e.g. median distance from each object to Q. The second problem is to retrieve the K nearest objects to Q based on overall distances in the ¿best population¿ with a given size specified by ¿-quantile for each object. While the first problem can be solved in polynomial time, we show that the 2nd problem is NP-hard. A set of efficient, novel algorithms have been proposed to give an exact solution for the first problem and an approximate solution for the second problem with the approximation ratio. Extensive experiment demonstrates that our techniques are very efficient and effective.

Uncertain data are inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In this paper, we formalize probabilistic reverse nearest neighbor query that is to retrieve the objects from the uncertain data that have higher probability than a given threshold to be the RNN of an uncertain query object. We develop an efficient algorithm based on various novel pruning approaches that solves the probabilistic RNN queries on multidimensional uncertain data. The experimental results demonstrate that our algorithm is even more efficient than a sampling-based approximate algorithm for most of the cases and is highly scalable.

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbor queries. Existing continuous reverse nearest neighbor monitoring techniques are sensitive towards objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor queries by assigning each object and query with a rectangular safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a by-product, our framework also reduces the communication cost in client-server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis to guide an effective selection of such rectangular safe regions. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.