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In this paper we study the evaluation of continuous constraint queries (CCQs) for spatiotemporal streams. A CCQ triggers an alert whenever a configuration of constraints between streaming events in space and time are satisfied. Consider, for instance, a server that receives updates from GPS-enabled agents that report their positions and other measurements (e.g., environmental readings). An example of CCQ is: "Alert whenever at least 5 readings closer than 5km to each other and within a time difference of 5 minutes report high pressures and low temperatures". We model CCQs as Constraint Satisfaction Problems (CSPs) and develop solutions for their continuous evaluation. Our techniques (1) consider the fast arrival rate of incoming events, and (2) minimize the memory requirements, without using predefined window constraints, but by utilizing the structure of the queries. In order to show the merits of the proposed techniques, we implement a system prototype and evaluate it with real data. [ABSTRACT FROM AUTHOR]

We address continuously processing an aggregation join query over data streams. Queries of this type involve both join and aggregation operations, with windows specified on join input streams. To our knowledge, the existing researches address join query optimization and aggregation query optimization as separate problems. Our observation, however, is that by putting them within the same scope of query optimization we can generate more efficient query execution plans. This is through more versatile query transformations, the key idea of which is to perform aggregation before join so join execution time may be reduced. This idea itself is not new (already proposed in the database area), but developing the query transformation rules faces a completely new set of challenges. In this paper, we first propose a query processing model of an aggregation join query with two key stream operators: (1) aggregation set update, which produces an aggregation set of tuples (one tuple per group) and updates it incrementally as new tuples arrive, and (2) aggregation set join, i.e., join between a stream and an aggregation set of tuples. Then, we introduce the concrete query transformation rules specialized to work with streams. The rules are far more compact and yet more general than the rules proposed in the database area. Then, we present a query processing algorithm generic to all alternative query execution plans that can be generated through the transformations, and study the performances of alternative query execution plans through extensive experiments. [ABSTRACT FROM AUTHOR]

Given two datasets A and B, their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair (a,b) in A ×B is in the result and (ii) the remaining pairs are determined by removing objects a,b from A,B respectively, and recursively searching for the next closest pair. An application of exclusive closest pairs is the computation of (car, parking slot) assignments. In this paper, we propose algorithms for the computation and continuous monitoring of ECP joins in memory, given a stream of events that indicate dynamic assignment requests and releases of pairs. Experimental results on a system prototype demonstrate the efficiency of our solutions in practice. [ABSTRACT FROM AUTHOR]

Spatio-temporal networks are spatial networks whose topology and parameters change with time. These networks are important due to many critical applications such as emergency traffic planning and route finding services and there is an immediate need for models that support the design of efficient algorithms for computing the frequent queries on such networks. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. In contrast, proposed time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations (e.g. connectivity, shortest path) are computationally more efficient than those for time expanded networks. One important query on spatio-temporal networks is the computation of shortest paths. Shortest paths can be computed either for a given start time or to find the start time and the path that leads to least travel time journeys (best start time journeys). Developing efficient algorithms for computing shortest paths in a time varying spatial network is challenging because these journeys do not always display greedy property or optimal substructure, making techniques like dynamic programming inapplicable. In this paper, we propose algorithms for shortest path computations in both contexts. We present the analytical cost models for the algorithms and provide an experimental comparison of performance with existing algorithms. [ABSTRACT FROM AUTHOR]

Finding hot routes (traffic flow patterns) in a road network is an important problem. They are beneficial to city planners, police departments, real estate developers, and many others. Knowing the hot routes allows the city to better direct traffic or analyze congestion causes. In the past, this problem has largely been addressed with domain knowledge of city. But in recent years, detailed information about vehicles in the road network have become available. With the development and adoption of RFID and other location sensors, an enormous amount of moving object trajectories are being collected and can be used towards finding hot routes. This is a challenging problem due to the complex nature of the data. If objects traveled in organized clusters, it would be straightforward to use a clustering algorithm to find the hot routes. But, in the real world, objects move in unpredictable ways. Variations in speed, time, route, and other factors cause them to travel in rather fleeting "clusters." These properties make the problem difficult for a naive approach. To this end, we propose a new density-based algorithm named FlowScan. Instead of clustering the moving objects, road segments are clustered based on the density of common traffic they share. We implemented FlowScan and tested it under various conditions. Our experiments show that the system is both efficient and effective at discovering hot routes. [ABSTRACT FROM AUTHOR]

In the consumer market, there has been an increasing interest in portable navigation systems in the last few years. These systems usually work on digital map databases stored on SD cards. As the price for these SD cards heavily depends on their capacity and as digital map databases are rather space-consuming, relatively high hardware costs go along with digital map databases covering large areas like Europe or the USA. In this paper, we propose new techniques for the compact storage of the most important part of these databases, i.e., the road network data. Our solution applies appropriate techniques from combinatorial optimization, e.g., adapted solutions for the minimum bandwidth problem, and from data mining, e.g., clustering based on suitable distance measures. In a detailed experimental evaluation based on real-world data, we demonstrate the characteristics and benefits of our new approaches. [ABSTRACT FROM AUTHOR]

The ability to continuously monitor the positions of mobile objects is important in many applications. While most past work has been set in Euclidean spaces, the mobile objects relevant in many applications are constrained to spatial networks. This paper addresses the problem of range monitoring of mobile objects in this setting, in which network distance is concerned. An architecture is proposed where the mobile clients and a central server share computation, the objective being to obtain scalability by utilizing the capabilities of the clients. The clients issue location reports to the server, which is in charge of data storing and query processing. The server associates each range monitoring query with the network-edge portions it covers. This enables incremental maintenance of each query, and it also enables shared maintenance of concurrent queries by identifying the overlaps among such queries. The mobile clients contribute to the query processing by encapsulating their host edge portion identifiers in their reports to the server. Extensive empirical studies indicate that the paper's proposal is efficient and scalable, in terms of both query load and moving-object load. [ABSTRACT FROM AUTHOR]

Mobile devices are increasingly being equipped with wireless peer-to-peer (P2P) networking interfaces, rendering the sharing of data among mobile devices feasible and beneficial. In comparison to the traditional client/server wireless channel, the P2P channels have considerably higher bandwidth. Motivated by these observations, we propose a collaborative spatial data sharing scheme that exploits the P2P capabilities of mobile devices. Using carefully maintained routing tables, this scheme enables mobile devices not only to use their local storage for query processing, but also to collaborate with nearby mobile peers to exploit their data. This scheme is capable of reducing the cost of the communication between mobile clients and the server as well as the query response time. The paper details the design of the data sharing scheme, including its routing table maintenance, query processing and update handling. An analytical cost model sensitive to user mobility is proposed to guide the storage content replacement and routing table maintenance. The results of extensive simulation studies based on an implementation of the scheme demonstrate that the scheme is efficient in processing location dependent queries and is robust to data updates. [ABSTRACT FROM AUTHOR]

In this work we study the construction of a framework for autonomous sites that are bound to spatial information and that form an overlay network; we investigate the parameters of such a distributed system in order to perform search guided by locality and directionality in space. We present the main parameters of the framework and propose appropriate values for them. A theoretical study discusses the overall search efficiency limits for two approaches concerning the main framework parameter, i.e. the distance metric. Furthermore, the behavior of the rest of the framework parameters is examined based on an experimental study. [ABSTRACT FROM AUTHOR]

Existing location-based services provide specialized services to their customers based on the knowledge of their exact locations. With untrustworthy servers, location-based services may lead to several privacy threats ranging from worries over employers snooping on their workers' whereabouts to fears of tracking by potential stalkers. While there exist several techniques to preserve location privacy in mobile environments, these techniques are limited as they do not distinguish between location privacy (i.e., a user wants to hide her location) and query privacy (i.e., a user can reveal her location but not her query). This distinction is crucial in many applications where the locations of mobile users are publicly known. In this paper, we go beyond the limitation of existing cloaking algorithms as we propose a new robust spatial cloaking technique for snapshot and continuous location-based queries that clearly distinguishes between location privacy and query privacy. By this distinction, we achieve two main goals: (1) supporting private location-based services to those customers with public locations, and (2) performing spatial cloaking on-demand basis only (i.e., when issuing queries) rather than exhaustively cloaking every single location update. Experimental results show that the robust spatial cloaking algorithm is scalable and efficient while providing anonymity for large numbers of continuous queries without hiding users' locations. [ABSTRACT FROM AUTHOR]

Due to the ease of digital copy, watermarking is crucial to protect the intellectual property of rights owners. We propose an effective watermarking method for vectorial geographical databases, with the focus on the buildings layer. Embedded watermarks survive common geographical filters, including the essential squaring and simplification transformations, as well as deliberate removal attempts, e.g. by noise addition, cropping or over-watermarking. Robustness against the squaring transformation is not adressed by existing approaches. The impact on the quality of the datasets, defined as a composition of point accuracy and angular quality, is assessed through an extensive series of experiments. Our method is based on a quantization of the distance between the centroid of the building and its extremal vertex according to its orientation. [ABSTRACT FROM AUTHOR]

In this paper we propose a fundamental approach to perform the class of Nearest Neighbor (NN) queries, the core class of queries used in many of the location-based services, without revealing the origin of the query in order to preserve the privacy of this information. The idea behind our approach is to utilize one-way transformations to map the space of all static and dynamic objects to another space and resolve the query blindly in the transformed space. However, in order to become a viable approach, the transformation used should be able to resolve NN queries in the transformed space accurately and more importantly prevent malicious use of transformed data by untrusted entities. Traditional encryption based techniques incur expensive O(n) computation cost (where n is the total number of points in space) and possibly logarithmic communication cost for resolving a KNN query. This is because such approaches treat points as vectors in space and do not exploit their spatial properties. In contrast, we use Hilbert curves as efficient one-way transformations and design algorithms to evaluate a KNN query in the Hilbert transformed space. Consequently, we reduce the complexity of computing a KNN query to $O(K\times\frac{2^{2N}}{n})$ and transferring the results to the client in O(K), respectively, where N, the Hilbert curve degree, is a small constant. Our results show that we very closely approximate the result set generated from performing KNN queries in the original space while enforcing our new location privacy metrics termed u-anonymity and a-anonymity, which are stronger and more generalized privacy measures than the commonly used K-anonymity and cloaked region size measures. [ABSTRACT FROM AUTHOR]

An uncertain geo-spatial dataset is a collection of geo-spatial objects that do not represent accurately real-world entities. Each object has a confidence value indicating how likely it is for the object to be correct. Uncertain data can be the result of operations such as imprecise integration, incorrect update or inexact querying. A k-route, over an uncertain geo-spatial dataset, is a path that travels through the geo-spatial objects, starting at a given location and stopping after visiting k correct objects. A k-route is considered shortest if the expected length of the route is less than or equal to the expected length of any other k-route that starts at the given location. This paper introduces the problem of finding a shortest k-route over an uncertain dataset. Since the problem is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Three methods for computing a short k-route are presented. The three methods are compared analytically and experimentally. For these three methods, experiments on both synthetic and real-world data show the tradeoff between the quality of the result (i.e., the expected length of the returned route) and the efficiency of the computation. [ABSTRACT FROM AUTHOR]

In many modern applications such as biometric identification systems, sensor networks, medical imaging, geology, and multimedia databases, the data objects are not described exactly. Therefore, recent solutions propose to model data objects by probability density functions(pdf). Since a pdf describing an uncertain object is often not explicitly known, approximation techniques like Gaussian mixture models(GMM) need to be employed. In this paper, we introduce a method for efficiently indexing and querying GMMs allowing fast object retrieval for arbitrary shaped pdf. We consider probability ranking queries which are very important for probabilistic similarity search. Our method stores the components and weighting functions of each GMM in an index structure. During query processing the mixture models are dynamically reconstructed whenever necessary. In an extensive experimental evaluation, we demonstrate that GMMs yield a compact and descriptive representation of video clips. Additionally, we show that our new query algorithm outperforms competitive approaches when answering the given probabilistic queries on a database of GMMs comprising about 100.000 single Gaussians. [ABSTRACT FROM AUTHOR]

Topological relationships between spatial objects are important for querying, reasoning, and indexing of data within spatial databases. These relationships are qualitative and respond to questions about the relative positions (e.g., disjointedness or containment) of spatial objects. Several models have been proposed that effectively define formal sets of topological relationships between simple spatial data types. The generalization of topological relationship models to complex spatial data types, which are roughly defined as multi-component versions of their simple counterparts, has raised awareness of the fact that these models only provide a global view of topological relationships whereas details of the topological relationships between individual components of the spatial objects involved are often ignored. In this paper, we introduce a fine-grained view on topological relationships between complex regions. Our model focuses on leveraging information about the local topological relationships that hold between the components of two spatial objects, thereby providing a localized view of the overall global topological relationship. [ABSTRACT FROM AUTHOR]

To achieve accurate results when matching geographic ontologies, it is important to have clear what has to be compared, and just then start comparing them. In this paper we define a geographic ontology reference model and, from it, the set of heterogeneities that may occur when comparing two geographic ontologies is elaborated, at both the concept and instance-level. Based on the heterogeneities set we then present the iG-Match, which consists in a software architecture that implements a methodology to perform geographic ontology matching, using some metrics specially developed for the geographic domain. [ABSTRACT FROM AUTHOR]

Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial $\mathcal{K}$ -anonymity is employed to hide the query initiator among a group of $\mathcal{K}$ users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of $\mathcal{K}$ users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users. [ABSTRACT FROM AUTHOR]

The notion of a map is a fundamental metaphor in spatial disciplines. However, there currently exist no adequate data models for maps that define a precise spatial data type for map geometries for use in spatial systems. In this paper, we consider a subclass of map geometries known as spatial partitions that are able to model maps containing region features. However, spatial partitions are defined using concepts such as infinite point sets that cannot be directly represented in computers. We define a graph theoretic model of spatial partitions, called spatial partition graphs, based on discrete concepts that can be directly implemented in spatial systems. [ABSTRACT FROM AUTHOR]

Massive data streams of positional updates become increasingly difficult to manage under limited memory resources, especially in terms of providing near real-time response to multiple continuous queries. In this paper, we consider online maintenance for spatiotemporal summaries of streaming positions in an aging-aware fashion, by gradually evicting older observations in favor of greater precision for the most recent portions of movement. Although several amnesic functions have been proposed for approximation of time series, we opt for a simple, yet quite efficient scheme that achieves contiguity along all retained stream pieces. To this end, we adapt an amnesic tree structure that effectively meets the requirements of time-decaying approximation while taking advantage of the succession inherent in positional updates. We further exemplify the significance of this scheme in two important cases: the first one refers to trajectory compression of individual objects; the other offers estimated aggregates of moving object locations across time. Both techniques are validated with comprehensive experiments, confirming their suitability in maintaining online concise synopses for moving objects. [ABSTRACT FROM AUTHOR]

In-network aggregation has been proposed as one of the main mechanisms for reducing messaging cost (and thus energy) in prior sensor network database research. However, aggregated values of a sensor field are of limited use in natural science domains because many phenomena, e.g., temperature and soil moisture, are actually continuous and thus best represented as a continuous surface over the sensor fields. Energy efficient collection of readings from all sensors became a focus in recent research literature. In this paper, we address the problem of interpolating maps from sensor fields. We propose a spatial autocorrelation aware, energy efficient, and error bounded framework for interpolating maps from sensor fields. Our work is inspired by spatial autocorrelation based interpolation models commonly used in natural science domains, e.g., kriging, and brings together several innovations. We propose a two round reporting framework that utilizes spatial interpolation models to reduce communication costs and enforce error control. The framework employs a simple and low overhead in-network coordination among sensors for selecting reporting sensors so that the coordination overhead does not eclipse the communication savings. We conducted extensive experiments using data from a real-world sensor network deployment and a large Asian temperature dataset to show that the proposed framework significantly reduces messaging costs. [ABSTRACT FROM AUTHOR]

With the increasing availability of spatial data in many applications, spatial clustering and outlier detection have received a lot of attention in the database and data mining community. As a very prominent method, the spatial scan statistic finds a region that deviates (most) significantly from the entire dataset. In this paper, we introduce the novel problem of mining regional outliers in spatial data. A spatial regional outlier is a rectangular region which contains an outlying object such that the deviation between the non-spatial attribute value of this object and the aggregate value of this attribute over all objects in the region is maximized. Compared to the spatial scan statistic, which targets global outliers, our task aims at local spatial outliers. We introduce two greedy algorithms for mining regional outliers, growing regions by extending them by at least one neighboring object per iteration, choosing the extension which leads to the largest increase of the objective function. Our experimental evaluation on synthetic datasets and a real dataset demonstrates the meaningfulness of this new type of outliers and the greatly superior efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Mobile services is emerging as an important application area for spatio-temporal database management technologies. Service users are often constrained to a spatial network, e.g., a road network, through which points of interest, termed data points, are accessible. Queries that implement services will often concern data points of some specific type, e.g., Thai restaurants or art museums. As a result, the relatively few data points are relevant to a query in comparison to the number of network edges, meaning that queries, e.g., k nearest-neighbor queries, must access large portions of the network. Existing query processing techniques pre-compute distances between data points and network vertices for improving the performance. However, pre- computation becomes problematic when the network or data points must be updated, possibly concurrently with the querying; and if the data points are moving, the existing techniques are inapplicable. In addition, multiple pre-computed structures must be maintained—one for each type of data point. We propose a versatile pre-computation approach for spatial network data. This approach uses a grid for pre-computing a simplified network. The above-mentioned shortcomings are avoided by making the pre-computed data independent of the data points. Empirical performance studies show that the structure is competitive with respect to the existing, more specialized techniques. [ABSTRACT FROM AUTHOR]

Similarity search algorithms that directly rely on index structures and require a lot of distance computations are usually not applicable to databases containing complex objects and defining costly distance functions on spatial, temporal and multimedia data. Rather, the use of an adequate multi-step query processing strategy is crucial for the performance of a similarity search routine that deals with complex distance functions. Reducing the number of candidates returned from the filter step which then have to be exactly evaluated in the refinement step is fundamental for the efficiency of the query process. The state-of-the-art multi-step k-nearest neighbor (kNN) search algorithms are designed to use only a lower bounding distance estimation for candidate pruning. However, in many applications, also an upper bounding distance approximation is available that can additionally be used for reducing the number of candidates. In this paper, we generalize the traditional concept of R-optimality and introduce the notion of RI-optimality depending on the distance information I available in the filter step. We propose a new multi-step kNN search algorithm that utilizes lower- and upper bounding distance information (Ilu) in the filter step. Furthermore, we show that, in contrast to existing approaches, our proposed solution is $R_{I_{lu}}$-optimal. In an experimental evaluation, we demonstrate the significant performance gain over existing methods. [ABSTRACT FROM AUTHOR]

In the k-medoid problem, given a dataset P, we are asked to choose k points in P as the medoids. The optimal medoid set minimizes the average Euclidean distance between the points in P and their closest medoid. Finding the optimal k medoids is NP hard, and existing algorithms aim at approximate answers, i.e., they compute medoids that achieve a small, yet not minimal, average distance. Similarly in this paper, we also aim at approximate solutions. We consider, however, the continuous version of the problem, where the points in P move and our task is to maintain the medoid set on-the-fly (trying to keep the average distance small). To the best of our knowledge, this work constitutes the first attempt on continuous medoid queries. First, we consider centralized monitoring, where the points issue location updates whenever they move. A server processes the stream of generated updates and constantly reports the current medoid set. Next, we address distributed monitoring, where we assume that the data points have some computational capabilities, and they take over part of the monitoring task. In particular, the server installs adaptive filters (i.e., permissible spatial ranges, called safe regions) to the points, which report their location only when they move outside their filters. The distributed techniques reduce the frequency of location updates (and, thus, the network overhead and the server load), at the cost of a slightly higher average distance, compared to the centralized methods. Both our centralized and distributed methods do not make any assumption about the data moving patterns (e.g., velocity vectors, trajectories, etc) and can be applied to an arbitrary number of medoids k. We demonstrate the efficiency and efficacy of our techniques through extensive experiments. [ABSTRACT FROM AUTHOR]

Recent years have revealed a growing importance of Virtual Reality (VR) visualization techniques which offer comfortable means to enable users to interactively explore 3D data sets. Particularly in the field of computational fluid dynamics (CFD), the rapidly increasing size of data sets with complex geometric and supplementary scalar information requires new out-of-core solutions for fast isosurface extraction and other CFD post-processing tasks. Whereas spatial access methods overcome the limitations of main memory size and support fast data selection, their VR support needs to be improved. Firstly, interactive users strongly depend on quick first views of the regions in their view direction and, secondly, they require quick relevant views even when they change their view point or view direction. We develop novel view-dependent extensions for access methods which support static and dynamic scenarios. Our new human vision-oriented distance function defines an adjusted order of appearance for data objects in the visualization space and, thus, supports quick first views. By a novel incremental concept of view-dependent result streaming which interactively follows dynamic changes of users' viewpoints and view directions, we provide a high degree of interactivity and mobility in VR environments. Our integration into the new index based graphics data server "IndeGS" proves the efficiency of our techniques in the context of post-processing CFD data with dynamically interacting users. [ABSTRACT FROM AUTHOR]

The one-shot shortest path query has been studied for decades. However, in the applications on road networks, users are actually interested in the path with the minimum travel time (the fastest path), which varies as time goes. This motivates us to study the continuous evaluation of fastest path queries in order to capture the dynamics of road networks. Repeatedly evaluating a large number of fastest path queries at every moment is infeasible due to its computationally expensive cost. We propose a novel approach that employs the concept of the affecting area and the tolerance parameter to avoid the reevaluation while the travel time of the current answer is close enough to that of the fastest path. Furthermore, a grid-based index is designed to achieve the efficient processing of multiple queries. Experiments on real datasets show significant reduction on the total amount of reevaluation and therefore the cost for reevaluating a query. [ABSTRACT FROM AUTHOR]