The Use of Spatial Metadata for Information Retrieval in Peer-to-Peer Networks

Geo-referenced data play an increasingly important role in many application areas. The result is a growing need for infrastructures that support the access and exchange of such data. Internet-based geodata servers and service-oriented geodata infrastructures focus on the exchange of GIS-type geodata, and require sophisticated server-side functionality. This prevents many small data providers from participating and hampers the exchange of directlyand indirectly geo-referenced data. The integration of peer-to-peer (P2P) technology may help to lower the barrier for participation. However, to support data retrieval based on complex spatial queries in such networks, condensed but expressive spatial metadata are needed. The paper outlines a concept for spatial metadata based on qualitative spatial reference models, and their application in P2P networks. 1. Geodata Servers and Geodata Infrastructures Until recently, geodata, i.e. data with an explicit reference to geographical space, were mainly used by specialists like geographers, cartographers, urban planners, and environmentalists. Today, geo-referenced data start to play an increasingly important role in many application areas, including marketing analysis, mobileand location-based services, and others. As a result, the number of both users and providers of geo-referenced data increases. This implies a growing need for the development of infrastructures for access to and exchange of georeferenced data. Many geodata providers have implemented internet-based servers that offer comfortable online access to digital maps and the associated attribute data. Such online geodata servers are typically run by government agencies like the Austrian Federal Environment Agency (http://gis.ubavie.gv.at/), or large GIS companies, like ESRI’s Arc Data Online server (http://www.esri.com/data/online/index.html). From a technical point of view, these services implement classic client-server architectures, using a combination of heavy-weight (geodata-, web-, and map-) servers, and light-weight WWW clients. Although these services work well to serve the data of one company or organisation, the integration of data distributed among multiple servers is difficult. An important step towards integrated and interoperable geodata is the development of geodata infrastructures and geodata brokers. An example for such an infrastructure is the emerging Geospatial Data Infrastructure in North-Rhine-Westfalia (GDI NRW), Germany (Kuhn, Basedow et al. 2000). The goal of the GDI NRW is to develop the GI market in NRW by linking the value chains of data users, data producers, service providers, service enablers, integrators, and infrastructure providers. The technological basis for the GDI NRW are standards established by the OpenGIS Consortium (OGC) (Buehler and McKee 1998). Following these standards, the GDI NRW adopts a service-oriented approach, establishing a network of providers of geo-dataand geo-services. In a geodata infrastructure based on the OGC specifications, the concept of a centralized client-server architecture is extended towards a network of distributed server nodes. Each server node offers specialized services, such as catalog services, web feature services, web coverage services or web mapping services (OGC 2002). Other specialized high-level service nodes like the BUSTER data broker which offers intelligent data retrieval, coupled with data integration and semantic translation services (Visser and Stuckenschmidt 2002), can be integrated as well. If a specific task requires the usage of several services, these can be linked together to form a process-chain. A number of network configurations are possible, but most of them involve some sort of central registration node that holds information about the status and properties of each service node in the network. In general, the geodata infrastructures described above rely on the cooperation of heavy-weight servers with light-weight clients. The server nodes provide complex services that require a sophisticated hardand software infrastructure, while the clients have only very limited functionality. We argue, that the high level of sophistication and financial investment needed to become an active member (i.e. a dataor service provider) in a geodata infrastructure will limit participation in the network to a number of government agencies and specialized private firms. Small companies (like architects or environmental consultants), and even more so individual users will not be able to share their potentially valuable geo-referenced data in such networks. For the typical application scenarios of geodata infrastructures this may not be a problem because they are designed to accommodate organizations that offer specialized data and services in the GIS domain. There is a problem, however, if the notion of “geodata” is extended from GIS-data to a more general definition of georeferenced data, and if the application scenarios for the distribution and exchange of such data include the growing fields of knowledge management intranets, and mobileor location-based services for the consumer market. In this paper, we will examine options to take advantage of peer-to-peer (P2P) data exchange network architectures for the exchange of geo-referenced data. We will focus our discussion on concepts for expressive and highly interoperable spatial metadata, which we believe are necessary to support intelligent spatial queries in such environments. 2. Peer-to-Peer (P2P) Networks A network paradigm used in an application area which so far had little to do with geo-referenced data may help to solve some of the problems mentioned above: The so-called “peer-to-peer” (P2P) networks (Oram 2001) are still mainly used as file-sharing networks on the consumer market. The probably best known P2P network, the music exchange network Napster (Napster 2001), acquired some doubtful fame lately when it had to be discontinued due to copyright violations. However, the P2P paradigm is not new. It’s origins date back to the original UUCP implementation of Usenet, and some of the backbone technologies of the Internet, like IP routing, follow a P2P architecture. Only recently has P2P technology become popular, and with Napster, Gnutella (Gnutella 2002), Morpheus (Morpheus 2002) and others, it threatens to re-shape some of the traditional marketing mechanisms, especially in the audio and video industry. The core paradigm of a “true” P2P network, like the Gnutella network, is that all significant communication takes place between cooperating peers, i.e. without the help and the control of a centralized server node. There is no pre-defined network structure, as nodes may join or leave the network based on their availability. The only fixed links between the nodes in the network are a set of common communication protocols. Because each node can be client and server at the same time, and because the size and complexity of the necessary client-server tool is generally small, it is easy to participate in a P2P network: By simply hosting the respective client-server tool, each computer linked to a TCP/IP network can become an active node in an overlaying P2P network. Not only can each node access data stored on other network nodes, but also contribute its own data and information to the network. This way, a P2P network forms a heterogeneous and highly distributed database, with every node contributing a small share to the common data pool. In the application domain of geo-referenced data, P2P network technology could help to lower the high obstacles created by the complexity and high costs of typical server solutions. An integration of “traditional” geodata infrastructures with P2P networks could help to de-couple services from data, enabling small companies and even individual users to contribute to the common data pool, while at the same time allowing specialized organizations to offer high-end, complex geoservices to process these data. The tremendous success of P2P networks for file sharing is proof for the ease-of-use and the consumer acceptance of the technology. Because the number of participants in such networks is large, there is a lot of redundancy in the distributed data. Therefore in most cases, sparsely specified queries and simple query matching algorithms are good enough to server the purpose. For example, a query specifying the string “material girl” will very likely result in a number of successful hits for the popular song by Madonna. However, the need for more meaningful metadata in support of complex queries is an issue even in the P2P community concerned with sharing audio and video files. Therefore some P2P systems, like Morpheus (Truelove and Chasin 2001), already offer the possibility to annotate data files with a limited set of metadata. In the domain of geo-referenced data expressive metadata will be even more important. Here, the number of participants of a data-sharing network will be much smaller, and the data more diverse. As a result, the amount of redundant information will be relatively low, and simple string matching of file names will not yield good results. To be useful for the exchange of geo-referenced data, standard P2P architectures and tools have to be modified to support the specification, exchange, and application of expressive metadata. The system has to support queries of the type concept @ location, i.e. queries that evaluate whether a data set is relevant with respect to a specific concept at a specific location. The level of relevance assigned to a data set depends on how closely the content of the data set matches the query. This takes into account direct matches of the specified concept and location, but also near matches which result from terminological specialization or generalization of the concept, as well as spatial generalizations of the location. In this paper, we outline methods to use spatial metadata for information retrieval w