Catálogo de publicaciones - libros

A Wide-Area Sensor Network (WASN) is a collection of heterogeneous sensor networks and data repositories spread over a wide geographic area. The diversity of sensor types and the regional differences over which WASNs operate result in semantic interoperability mismatches among sensor data, and a difficulty in agreeing on methods for sensor data access and exchange. We assume that sensors and their associated data have an explicit spatio-temporal basis (or tagging) in their representation. In this paper, we describe a spatio-temporal loosely-coupled federated database model for the WASN data storage problem - that of unifying query and data representation given a heterogeneous WASN - and propose a conceptual schema to ease the problem of integration of sensor data representations. This is a continuing and critical challenge as sensor networks become more ubiquitous and data interoperation becomes increasing vital for a variety of applications (such as homeland security, transportation, environmental monitoring, etc.). We employ a top-down ontology-driven software development methodology. We use the SNAP/SPAN ontology as a sample framework for the conceptual schema. We compare our methodology of conceptual schema development with a bottom-up entity-oriented schema construction and discuss the differences in the two approaches. A unique contribution is the discussion of deployment experiences to evaluate proposed approaches in the context of a concrete WASN testbed.

Spatio-temporal network is defined by a set of nodes, and a set of edges, where the properties of nodes and edges may vary over time. Such networks are encountered in a variety of domains ranging from transportation science to sensor data analysis. Given a spatio-temporal network, the aim is to develop a model that is simple, expressive and storage efficient. The model must also provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. 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. This model is generally used to represent time-dependent flow networks and tends to be application-specific in nature. In contrast, the 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. Our approach achieves physical data independence and also addresses the issue of modeling spatio-temporal networks that do not involve flow parameters. In this paper, we describe the model at the conceptual, logical and physical levels. We also present case studies from various application domains.

The last few years have seen a growing interest in approaches that define methodologies to automatically extract semantics from databases by using ontologies. Geographic data are very rarely collected in a well organized way, quite often they lack both metadata and conceptual schema. Extracting semantic information from data stored in a geodatabase is complex and an extension of the existing methodologies is needed. We describe an approach to extracting a geospatial ontology from geographical data stored in spatial databases. To provide geospatial semantics we introduce new relations which define geospatial ontology that can serve as a basis for an advanced user querying system. Some examples of use of the methodology in the urban domain are presented.

Grounding an ontology upon geographical data has been proposed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason about features within the data.

Spatial and temporal data are critical components in many applications. This is especially true in analytical domains such as national security and criminal investigation. Often, the analytical process requires uncovering and analyzing complex thematic relationships between disparate people, places and events. Fundamentally new query operators based on the graph structure of Semantic Web data models, such as semantic associations, are proving useful for this purpose. However, these analysis mechanisms are primarily intended for thematic relationships. In this paper, we describe a framework built around the RDF metadata model for analysis of thematic, spatial and temporal relationships between named entities. We discuss modeling issues and present a set of semantic query operators. We also describe an efficient implementation in Oracle DBMS and demonstrate the scalability of our approach with a performance study using a large synthetic dataset from the national security domain.

The paper presents an approach to verifying the consistency of generalized geospatial data at a conceptual level. The principal stages of the proposed methodology are Analysis, Synthesis, and Verification. Analysis is focused on extracting the peculiarities of spatial relations by means of quantitative measures. Synthesis is used to generate a conceptual representation (ontology) that explicitly and qualitatively represents the relations between geospatial objects, resulting in tuples called herein semantic descriptions. Verification consists of a comparison between two semantic descriptions (description of source and generalized data): we measure the semantic distance (confusion) between ontology local concepts, generating three global concepts Equal, Unequal, and Equivalent. They measure the (in) consistency of generalized data: Equal and Equivalent – their consistency, while Unequal – an inconsistency. The method does not depend on coordinates, scales, units of mea-sure, cartographic projection, representation format, geometric primitives, and so on. The approach is applied and tested on the generalization of two topographic layers: rivers and elevation contour lines (case of study).

Study of semantics in the context of Geographic Information Systems (GISs) usually focuses on association of meaning with spatial data that constitute the input to these systems. The goal is to create new data models that enable richer interaction with GISs. In this paper we widen the perspective of such studies and explore the implications of associating semantics with map styles that are present implicitly in the output of GISs. Traditionally in computer science style of rendering/presenting data has been viewed as extraneous information that does not add semantic value to data. In GIS however, several styles of rendering maps are possible, and these styles are often motivated by functionality. Thus a map style has a strong association with meaning. In this paper we explore the methods of associating semantics with the style of a map. We discuss the various levels at which semantic associations with map styles can be established, and how this lead to creation of new map rendering styles that exhibit coherence in visualization of spatial information.

The traditional Web services architecture uses a keyword based search to match a query to one or more service providers. However, a world-to-word matching to discover a service provider is too simplistic for geospatial data and fails to capture matches that advertise their functionality using domain-dependent terminology. In this paper, we present DAGIS (Discovering Annotated Geospatial Information Services) – a semantic Web services based framework for geospatial domain that has graphical interface to query and discover services. It handles the semantic heterogeneities involved in the discovery phase and we propose algorithms for selecting the best service through QoS (Quality of Service) based semantic matching. The framework is capable of performing dynamic compositions on the fly through a back chaining algorithm. The framework is evaluated by solving queries posed by users in various geospatial decision making scenarios.

Ontologies can be tailored in ways that can facilitate the description of workflows by specifying how concepts representing services are used to access and create concepts representing data and products. Early work on the development of such ontologies, and reported in this paper, has resulted in the construction of a gravity data ontology. The relationships that are defined in the ontology capture inputs and outputs of methods, e.g., derived data and products, as well as other associations that are related to workflow computation. This paper presents the basis for a computation-driven ontology that evolved into the workflow-driven ontology approach. In addition, the paper describes the process used to construct an ontology for gravity data using the computation-driven approach, and it presents a gravity ontology that documents the processes and methods associated with gravity data and related products.