In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network.

Reliable propagation of information through large networks, e.g., communication networks, social networks, or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.

Due to the availability of large transportation (e.g., road network sensor data) and transportation-related (e.g., pollution, crime) data as well as the ubiquity of car navigation systems, recent route planning techniques need to optimize for multiple criteria (e.g., travel time or distance, utility/value such as safety or attractiveness). In this paper, we introduce a novel problem called Twofold Time-Dependent Arc Orienteering Problem (2TD-AOP), which seeks to find a path from a source to a destination maximizing an accumulated value (e.g., attractiveness of the path) while not exceeding a cost budget (e.g., total travel time). 2TD-AOP has many applications in spatial crowdsourcing, real-time delivery, and online navigation systems (e.g., safest path, most scenic path). Although 2TD-AOP can be framed as a variant of AOP, existing AOP approaches cannot solve 2TD-AOP accurately as they assume that travel-times and values of network edges are constant. However, in real-world the travel-times and values are time-dependent, where the actual travel time and utility of an edge depend on the arrival time to the edge. We first discuss the practicality of this novel problem by demonstrating the benefits of considering time-dependency, empirically. Subsequently, we show that optimal solutions are infeasible (NP-hard) and solutions to the static problem are often invalid (i.e., exceed the cost budget). Therefore, we propose an efficient approximate solution with spatial pruning techniques, optimized for fast response systems. Experiments on a large-scale, fine-grained, real-world road network demonstrate that our approach always produces valid paths, is orders of magnitude faster than any optimal solution with acceptable accumulated value.

An inherent challenge arising in any dataset containing information of space and/or time is uncertainty due to various sources of imprecision. Integrating the impact of the uncertainty is a paramount when estimating the reliability (confidence) of any query result from the underlying input data. To deal with uncertainty, solutions have been proposed independently in the geo-science and the data-science research community. This interdisciplinary tutorial bridges the gap between the two communities by providing a comprehensive overview of the different challenges involved in dealing with uncertain geo-spatial data, by surveying solutions from both research communities, and by identifying similarities, synergies and open research problems.

The proliferation of the Web 2.0 and the ubiquitousness of social media yield a huge flood of heterogenous data that is voluntarily published and shared by billions of individual users all over the world. As a result, the representation of an entity (such as a real person) in this data may consist of various data types, including location and other numeric attributes, textual descriptions, images, videos, social network information and other types of information. Searching similar entities in this multi-enriched data exploiting the information of multiple representations simultaneously promises to yield more interesting and relevant information than searching among each data type individually. While efficient similarity search on single representations is a well studied problem, existing studies lacks appropriate solutions for multi-enriched data taking into account the combination of all representations as a whole. In this paper, we address the problem of index-supported similarity search on multi-enriched (a.k.a. multi-represented) objects based on a set of metrics, one metric for each representation. We define multimetric similarity search queries by employing user-defined weight function specifying the impact of each metric at query time. Our main contribution is an index structure which combines all metrics into a single multi-dimensional access method that works for arbitrary weights preferences. The experimental evaluation shows that our proposed index structure is more efficient than existing multi-metric access methods considering different cost criteria and tremendously outperforms traditional approaches when querying very large sets of multi-enriched objects.

Due to the availability of large historical and real-time traffic data, car navigation systems are becoming more and more advanced in predicting the travel time for various routes and finding the fastest route from a source to a destination given a start time. The most advanced of these systems predict the travel time of the routes, given past traffic patterns in order to find the best route. However, the best route is not necessarily a reliable route as well, i.e., the route with the least variation in possible travel times. The most reliable route is desirable when traveling with a deadline, e.g., to reach a flight at the airport or to arrive on time for an important meeting. To find the most reliable route, one needs to predict the probability distribution of travel times for that route. This in turn requires the estimation of travel time probability distributions for each and every link, given a link-entrance-time. In this paper we address the problem of computing these link travel time distributions. To the best of our knowledge there has not been any study on how to compute probability distributions for links (/edges) in road networks. We show how this first step can affect the accuracy of the travel time distribution over the entire route. Our final challenge is to evaluate the result of different approaches in computing these travel time distributions, which is difficult because the reported travel time is not a single value but a probabilistic distribution highly depending on the trip start time. We thus propose a statistical test that enables us to evaluate these outcomes.

The always increasing number of videos on the internet yield data for novel quite useful multimedial service applications, but finding videos best satisfying the users need is becoming challenging. At the same time, new video collection platforms allow to upload videos enriched with positional metadata when recorded with a GPS-enabled device such as a smartphone. These platforms can thus go beyond the prevalent keyword search and instead take advantage from the positional metadata of videos, e.g., to find videos recorded in a certain area. This information, however allows for much more interesting queries. In this paper we present Video Route which allows a user to specify a target route (query) and obtain an approximation of the target route that is piecewise composed of subtrajectories derived from a set of given trajectories. Our approach is aimed at high approximation accuracy while keeping the number of composed subtrajectories low.

Recently spatial crowd sourcing was introduced as a natural extension to traditional crowd sourcing allowing for tasks to have a geospatial component, i.e., A task can only be performed if a worker is physically present at the location of the task. The problem of assigning spatial tasks to workers in a spatial crowd sourcing system can be formulated as a weighted bipartite b-matching graph problem that can be solved optimally by existing methods for the minimum cost maximum flow problem. However, these methods are still too complex to run repeatedly for an online system, especially when the number of incoming workers and tasks increases. Hence, we propose a class of approaches that utilizes an online partitioning method to reduce the problem space across a set of cloud servers to construct independent bipartite graphs and solve the assignment problem in parallel. Our approaches solve the spatial task assignment approximately but competitive to the exact solution. We experimentally verify that our approximate approaches outperform the centralized and Map Reduce version of the exact approach with acceptable accuracy and thus suitable for online spatial crowd sourcing at scale.

Fuzzy object databases are becoming more and more important in the context of image analysis. Examples include satellite images where blurred trees, houses or lakes can still be organized and searched in a meaningful manner and biomedical images which can be utilized to find similar disease patterns and monitor disease progress. One problem of the underlying data is that it contains blurred image content, i.e., fuzzy data. Therefore, an image-based similarity search, which can process huge amounts of fuzzy data in an efficient and effective way, is desirable. The aim of this work is to develop efficient and effective methods for similarity search in fuzzy object databases. First, a suitable similarity measure based on a shape similarity is proposed. Based on this, two novel k-nearest neighbor algorithms for efficient similarity search are presented. The first approach gains efficiency at the cost of incurring only approximate results, while the second approach uses a filter-refinement approach to prune computation. Our experimental evaluation shows the efficiency of the proposed algorithms.

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty---using different uncertainty models---on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.

Location-related data has a tremendous impact in many applications of high societal relevance and its growing volume from heterogeneous sources is one true example of a Big Data [1]. An inherent property of any spatio-temporal dataset is uncertainty due to various sources of imprecision. This tutorial provides a comprehensive overview of the different challenges involved in managing uncertain spatial and spatio-temporal data and presents state-of-the-art techniques for addressing them.

This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.

This demonstration presents our Uncertain-Spatio-Temporal (UST)} framework that we have developed in recent years. The framework allows not only to visualize and explore spatio-temporal data consisting of (location, time, object)-triples but also provides an extensive codebase easily extensible and customizable by developers and researchers. The main research focus of this UST-framework is the explicit consideration of uncertainty, an aspect that is inherent in spatio-temporal data, due to infrequent position updates, due to physical limitations and due to power constraints. The UST-framework can be used to obtain a deeper intuition of the quality of spatio-temporal data models. Such models aim at estimating the position of a spatio-temporal object at a time where the object's position is not explicitly known, for example by using both historic (traffic-) pattern information, and by using explicit observations of objects. The UST-framework illustrates the resulting distributions by allowing a user to move forward and backward in time. Additionally the framework allows users to specify simple spatio-temporal queries, such as spatio-temporal window queries and spatio-temporal nearest neighbor (NN) queries. Based on recently published theoretic concepts, the UST-framework allows to visually explore the impact of different models and parameters on spatio-temporal data. The main result showcased by the UST-framework is a minimization of uncertainty by employing stochastic processes, leading to small expected distances between ground truth trajectories and modelled positions.

This work tackles the management of novel types of inconsistencies in Spatio-Temporal Databases, different from traditional database settings where integrity constraints pertain to the explicitly stored (or, defined via views and aggregates) values. We observe that spatio-temporal data has its specific types of ßemanticconstraints and we aim at minimization of the changes needed for repairing their violations.

In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point pϵR, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neighbor of q. However, there is no efficient algorithm for computing an exact PV-cell. We hence study how to derive an axis-parallel hyper-rectangle (called the Uncertain Bounding Rectangle, or UBR) that tightly contains a PV-cell. We further develop the PV-index, a structure that stores UBRs, to evaluate probabilistic nearest neighbor queries over uncertain data. An advantage of the PV-index is that upon updates on S, it can be incrementally updated. Extensive experiments on both synthetic and real datasets are carried out to validate the performance of the PV-index.

Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatio-temporal data analysis. More recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been proposed, which do not allow for accurate probabilistic search. In this paper, we fill this gap by addressing probabilistic nearest neighbor queries in databases with uncertain trajectories modeled by stochastic processes, specifically the Markov chain model. We study three nearest neighbor query semantics that take as input a query state or trajectory q and a time interval, and theoretically evaluate their runtime complexity. Furthermore we propose a sampling approach which uses Bayesian inference to guarantee that sampled trajectories conform to the observation data stored in the database. This sampling approach can be used in Monte-Carlo based approximation solutions. We include an extensive experimental study to support our theoretical results.

Ranking queries have been investigated extensively in the past due to their broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. To the best of our knowledge, we present the first efficient approach for similarity ranking in fuzzy object databases. The main challenge of ranking fuzzy objects is that these objects consist of multiple instances, each associated with a probability. We propose a framework to transform fuzzy objects into probabilistic objects which can then be ranked using existing algorithms for probabilistic objects.

The advances in sensing and telecommunication technologies allow the collection and management of vast amounts of spatio-temporal data combining location and time information.Due to physical and resource limitations of data collection devices (e.g., RFID readers, GPS receivers and other sensors) data are typically collected only at discrete points of time. In-between these discrete time instances, the positions of tracked moving objects are uncertain. In this work, we propose novel approximation techniques in order to probabilistically bound the uncertain movement of objects; these techniques allow for efficient and effective filtering during query evaluation using an hierarchical index structure.To the best of our knowledge, this is the first approach that supports query evaluation on very large uncertain spatio-temporal databases, adhering to possible worlds semantics. We experimentally show that it accelerates the existing, scan-based approach by orders of magnitude.

This demo presents a framework for running probabilistic graph queries on uncertain graphs and visualizing their results. The framework supports the most common uncertainty model for uncertain graphs, i.e. existential uncertainty for the edges of the graph. A large variety of meaningful graph queries are supported, such as shortest path, range, kN, reverse kN, reachability and various aggregation queries. Since the problem of exact probability computation according to possible world semantics is in #P-Time for many combinations of model and query, and since ignoring uncertainty (e.g. by using expectations only) will yield counterintuitive and hard to interpret results, our framework uses an optimized version of Monte-Carlo sampling to estimate the results which allows us not only to perform queries that conform to possible world semantics but also to sample only parts of a graph relevant for a given query. The main strength of this framework is the visualization combined with statistic hypothesis tests, which gives the user not only the estimated result of a query, but also an indication of how significant and reliable these results are. The aim of this demonstration is to give an intuition that a sampling based approach to probabilistic graphs is viable, and that the estimated results quickly converge even for very large graphs. A video demonstrating our framework can be downloaded at http://www.dbs.ifi.lmu.de/Publikationen/videos/PGraph.html

The problem of modeling and managing uncertain data has received a great deal of interest, due to its manifold applications in spatial, temporal, multimedia and sensor databases. There exists a wide range of work covering spatial uncertainty in the static (snapshot) case, where only one point of time is considered. In contrast, the problem of modeling and querying uncertain spatio-temporal data has only been treated as a simple extension of the spatial case, disregarding time dependencies between consecutive timestamps. In this work, we present a framework for efficiently modeling and querying uncertain spatio-temporal data. The key idea of our approach is to model possible object trajectories by stochastic processes. This approach has three major advantages over previous work. First it allows answering queries in accordance with the possible worlds model. Second, dependencies between object locations at consecutive points in time are taken into account. And third it is possible to reduce all queries on this model to simple matrix multiplications. Based on these concepts we propose efficient solutions for different probabilistic spatio-temporal queries. In an experimental evaluation we show that our approaches are several order of magnitudes faster than state-of-the-art competitors.

In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach supports a general uncertainty model using continuous probabilistic density functions to describe the (possibly correlated) uncertain attributes of objects. In a nutshell, the problem to be solved is to compute the PDF of the random variable denoted by the probabilistic domination count: Given an uncertain database object B, an uncertain reference object R and a set D of uncertain database objects in a multi-dimensional space, the probabilistic domination count denotes the number of uncertain objects in D that are closer to R than B. This domination count can be used to answer a wide range of probabilistic similarity queries. Specifically, we propose a novel geometric pruning filter and introduce an iterative filter-refinement strategy for conservatively and progressively estimating the probabilistic domination count in an efficient way while keeping correctness according to the possible world semantics. In an experimental evaluation, we show that our proposed technique allows to acquire tight probability bounds for the probabilistic domination count quickly, even for large uncertain databases.

Given a query object q , a reverse nearest neighbor (RNN) query in a common certain database returns the objects having q as their nearest neighbor. A new challenge for databases is dealing with uncertain objects. In this paper we consider probabilistic reverse nearest neighbor (PRNN) queries, which return the uncertain objects having the query object as nearest neighbor with a sufficiently high probability. We propose an algorithm for efficiently answering PRNN queries using new pruning mechanisms taking distance dependencies into account. We compare our algorithm to state-of-the-art approaches recently proposed. Our experimental evaluation shows that our approach is able to significantly outperform previous approaches. In addition, we show how our approach can easily be extended to PR k NN (where k > 1) query processing for which there is currently no efficient solution.

Similarity queries are an important query type in multimedia databases. To implement these types of queries, database systems often use spatial index structures like the R*-Tree. However, the majority of performance evaluations for spatial index structures rely on a conventional background storage layer based on conventional hard drives. Since newer devices like solid-state-disks (SSD) have a completely different performance characteristic, it is an interesting question how far existing index structures profit from these modern storage devices. In this paper, we therefore examine the performance behaviour of the R*-Tree on an SSD compared to a conventional hard drive. Testing various influencing factors like system load, dimensionality and page size of the index our evaluation leads to interesting insights into the performance of spatial index structures on modern background storage layers.

In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server architecture. The RkNN monitoring query computes for a given query object q, the set RkNN(q) of objects having q as one of their k-nearest neighbors for each point in time. In our setting the central server can poll the exact positions of the clients if needed. However in contrast to most existing approaches for this problem we argue that in various applications, the limiting factor is not the computational time needed but the amount of traffic sent via the network. We propose an approach that minimizes the amount of communication between clients and central server by an intelligent approximation of the position of the clients. Additionally we propose several poll heuristics in order to further decrease the communication costs. In the experimental section we show the significant impact of our proposed improvements to our basic algorithm.

Fast query processing of complex objects, e.g. spatial or uncertain objects, depends on efficient spatial pruning of objects' approximations, which are typically minimum bounding rectangles (MBRs). In this paper, we propose a novel effective and efficient criterion to determine the spatial topology between multi-dimensional rectangles. Given three rectangles R, A, and B, in a multi-dimensional space, the task is to determine whether A, is definitely closer to R, than B. This domination relation is used in many applications to perform spatial pruning. Traditional techniques apply spatial pruning based on minimal and maximal distance. These techniques however show significant deficiencies in terms of effectivity. We prove that our decision criterion is correct, complete, and efficient to compute even for high dimensional databases. In addition, we tackle the problem of computing the number of objects dominating an object o. The challenge here is to incorporate objects that only partially dominate o. In this work we will show how to detect such partial domination topology by using a modified version of our decision criterion. We propose strategies for conservatively and progressively estimating the total number of objects dominating an object. Our experiments show that the new pruning criterion, albeit very general and widely applicable, significantly outperforms current state-of-the-art pruning criteria.

In this paper, we formalize the novel concept of Constrained Reverse k-Nearest Neighbor (CRkNN) search on mobile objects (clients) performed at a central server. The CRkNN query computes for a given query object q the set RkNN(q) of objects having q as one of their k-nearest neighbors, iff the result set exceeds a specific threshold m, i.e. Card(RkNN(q)) ≥ m. Otherwise, the query reports an empty result. In our setting, the positions of the query object and database objects are approximated by minimal bounding rectangles that depend on the last reported location of the object, as well as on the time that has been passed since the object reported its recent exact location. We propose an approach that minimizes the amount of communication between clients and central server by using the approximation of the positions to identify true hits and true drops. We present a multi-step filter/refinement framework that uses a novel refinement heuristic to minimize the number of objects that are required to provide their exact location. Our solution does not assume any preprocessing steps which makes it applicable for dynamic environments where updates of the database frequently occur. Experiments show that our approach considerably reduces the communication load compared to existing approaches designed for traditional reverse nearest neighbor search in static data.