Abstract
Spatial data are increasingly becoming available on the Internet in applications such as routing portals that involve map-based and satellite imagery backgrounds, allowing a large audience to access and share the rich databases that are currently used by the specialized geographic community. These spatial data are heterogeneous, being available in various formats, and stored in disparate formats (flat files, relational or object-oriented databases, etc.). Some data are structured according to well-established data modeling techniques such as the relational or object-oriented data models; other data, such as data maintained in various information systems, spreadsheets, or Internet repositories, are in proprietary formats, semistructured, or unstructured. In practice, this situation of multiple models and schemas combined with the difficulty for establishment agreements for data representation in the application domains becomes spatial data in special regarding other types of scientific data, making the interoperability problem a nontrivial task (Lemmens, Wytzisk, de By, Granell, Gould & van Oosterom, 2006). In addition to the scale of data integration, the complex and heterogeneous query processing and domain-specific computational capabilities supported by these sources make spatial data integration a real challenge (Boulcema, Essid & Lacroix, 2002; Devogele, Parent & Spaccapietra, 1998; Goodchild, Egenhofer, Fegeas & Kottman, 1999).
TopIntroduction
Spatial data are increasingly becoming available on the Internet in applications such as routing portals that involve map-based and satellite imagery backgrounds, allowing a large audience to access and share the rich databases that are currently used by the specialized geographic community. These spatial data are heterogeneous, being available in various formats, and stored in disparate formats (flat files, relational or object-oriented databases, etc.). Some data are structured according to well-established data modeling techniques such as the relational or object-oriented data models; other data, such as data maintained in various information systems, spreadsheets, or Internet repositories, are in proprietary formats, semi-structured, or unstructured. In practice, this situation of multiple models and schemas combined with the difficulty for establishment agreements for data representation in the application domains becomes spatial data in special regarding other types of scientific data, making the interoperability problem a nontrivial task (Lemmens, Wytzisk, de By, Granell, Gould & van Oosterom, 2006). In addition to the scale of data integration, the complex and heterogeneous query processing and domain-specific computational capabilities supported by these sources make spatial data integration a real challenge (Boulcema, Essid & Lacroix, 2002; Devogele, Parent & Spaccapietra, 1998; Goodchild, Egenhofer, Fegeas & Kottman, 1999).
Historically, due to specialized characteristics and the nature of spatial data, geographic information systems (GISs) were managed separately from existing database systems. As first steps to spatial data integration in the mid-1990s, advances in database technology enabled accommodating spatial data in relational databases, allowing organizations to take the first steps toward enterprise GIS and the elimination of organizational “spatial data islands” (ESRI, 2003). Some examples are the appearance of Oracle Spatial (www.mysql.com). The early work for spatial data integration in database systems focused on sharing simple spatial features in a relational database. Then, standard data manipulation languages such as SQL (Structured Query Language) began to adopt common spatial functionalities to embed, for example, spatial selections and topological queries in SQL statements. The arrival of the first relational models capable of storing both spatial and attribute data led to spatial databases (Rigaux, Scholl & Voisard, 2001), which provided methods for spatial data modeling, algorithms, access methods, and query processing extending traditional database systems.
The success factor of Web services technology has permitted promoting service integration and interoperability among heterogeneous distributed information sources. The GIS approach to service-oriented architecture (SOA) is represented by the Spatial Data Infrastructure (SDI) paradigm, which offers the possibility to access distributed, heterogeneous spatial data through a set of policies, common rules, and standards that facilitate interconnecting spatial information users in an interoperable way (Granell, Gould, Manso & Bernabé, 2007b).
Key Terms in this Chapter
Service: Functionality provided by a service provider through interfaces (paraphrased from ISO 19119—Geographic Information Services).
GML: Geography Markup Language is an XML grammar defined by OGC to express geographical features. To help users and developers structure and facilitate the creation of GML-based application, GML provides GML profiles that are XML schemas that extend the very GML specification in a modular fashion. A GML profile is a GML subset for a concrete context or application but without the need for the full GML grammar, thus simplifying the adoption of GML and facilitating its rapid usage. Some common examples of GML profiles that have been published are Point Profile for applications with point geometric data and GML Simple Features profile, supporting vector feature requests and responses as the case of WFS.
OGC: Open Geospatial Consortium (http://www.opengeospatial.org), a membership body of 300-plus organizations from the commercial, government, and academic sectors that creates consensus interface specifications in an effort to maximize interoperability among software detailing with geographic data.
SDI: Spatial Data Infrastructure. Many government administrations have initiated coordinated actions to facilitate the discovery and sharing of spatial data, creating the institutional basis for SDI creation. The Global Spatial Data Infrastructure (GSDI) association (http://www.gsdi.org) defines SDI as a coordinated series of agreements on technology standards, institutional arrangements, and policies that enable the discovery and facilitate the availability of and access to spatial data. The SDI, once agreed upon and implemented, serves to connect Geographic Information Systems (GIS) and other spatial data users to a myriad of spatial data sources, the majority of which are held by public sector agencies.
Feature: The fundamental unit of geospatial information. For example, depending on the application, a feature could be any part of the landscape, whether natural (such as a stream or ridge) or artificial (such as a road or power line). A feature object then corresponds to a real-world or abstract entity. Attributes of this feature object describe measurable or describable phenomena about this entity. Feature object instances derive their semantics and valid use or analysis from the corresponding real-world entities’ meaning.
Mediator: A negotiator who acts as a link between parties, the neutral who carries out the dispute resolution process called mediation.
WFS: The OpenGIS Web Feature Service specification allows a client to retrieve and update geospatial data encoded in Geography Markup Language (GML) from multiple WFS. The specification defines interfaces for data access and manipulation operations on geographic features using HTTP as the distributed computing platform. Via these interfaces, a Web user or service can combine, use, and manage geodata, the feature information behind a map image, from various sources.
Wrapper: A package that changes the interface to an existing package without substantially increasing its functionality.