Catálogo de publicaciones - libros

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 , which consists in a software architecture that implements a methodology to perform geographic ontology matching, using some metrics specially developed for the geographic domain.

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 spatial data types. The generalization of topological relationship models to 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 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 topological relationships that hold between the components of two spatial objects, thereby providing a view of the overall global topological relationship.

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 -anonymity is employed to hide the query initiator among a group of users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time.

In this paper we propose , a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. 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 users that are consecutive in the 1-D space. Compared to existing state-of-the-art, 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 has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.

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 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 () computation cost (where 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 and transferring the results to the client in (), respectively, where , 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 and , which are stronger and more generalized privacy measures than the commonly used -anonymity and cloaked region size measures.

Existing location-based services provide specialized services to their customers based on the 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 spatial cloaking technique for and 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.

An uncertain geo-spatial dataset is a collection of geo-spatial objects that do not represent accurately real-world entities. Each object has a 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 , 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 correct objects. A -route is considered shortest if the expected length of the route is less than or equal to the expected length of any other -route that starts at the given location. This paper introduces the problem of finding a shortest -route over an uncertain dataset. Since the problem is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution,  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 -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 (, the expected length of the returned route) and the efficiency of the computation.

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.

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.

We address continuously processing an 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 problems. Our observation, however, is that by putting them within the scope of query optimization we can generate more efficient query execution plans. This is through more versatile , 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.

In this paper we study the evaluation of (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 . 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.