Abstract. One of the main drawbacks of the Semantic Web is the lack of semantically rich data, since most of the information is still stored in relational databases. In this paper, we present an approach to map legacy data stored in relational databases into the Semantic Web using virtually any modern RDF query language, as long as it is closed within RDF. Consequently, a Semantic Web developer does not need to learn and adopt a new mapping language, but he may perform the mapping task using his preferred RDF query language. MotivationDespite the vision of a Semantic Web [6] and many efforts helping to realize it, the actual Semantic Web still lacks of enough semantic data. Most information is still modeled and stored in relational databases and thus out of reach for many Semantic Web applications. As a consequence, such applications need to create a corresponding mapping between the relational and the semantic models by themselves for being able to access relational data. Realizing this situation, some efforts have arisen to straighten out this deplorable situation. Most approaches translate relational data into a Semantic Web representation using a proprietary mapping language (cf. Section 2). In [19] we have introduced Relational.OWL, our technique to automatically transform relational data into a machine processable and understandable representation (cf. Section 3.1). Nevertheless, such a representation does not include real semantics, since it converts the schema of a database automatically into an ontology and the data items as its instances, i.e. the data is described as it was in the database. For many Semantic Web applications, this is a reasonable technique, since they are able to quickly access legacy data stored in a relational database using their own built-in functionality. However, such a representation could be inappropriate, if the data has to be processed for further reasoning tasks. In this paper we present how to map relational data into the Semantic Web using virtually any modern RDF query language, as long as the language is closed within RDF, i.e. it returns valid RDF graphs as query results. For this purpose, data and schema components of the original relational database are
Modern intra-and inter-enterprise collaboration requires access to information spread over multiple autonomous and heterogeneous data sources. In this paper we present a loosely coupled multidatabase architecture enhanced with P2P concepts. It achieves a reasonable tradeoff between autonomy and information sharing among both, permanently available and volatile data sources. Each data node decides autonomously which kind of information to share. Data availability, query performance, and up-to-dateness on each participating data node is improved using a pushbased replication strategy, which propagates data modifications over multiple nodes. MotivationSince the first centralized databases found their way into the enterprises in the late 60s, the needs and requirements have changed towards a more distributed management of data. Today there are many corporations which possess a large amount of databases, often spread over different regions or countries and generally connected to a network. These local databases typically raised in an autonomous and independent manner fitting the special needs of the users at the local site. This leads to logical and physical differences in the databases concerning data formats, concurrency control, the data manipulation language, or the data model [8]. It is crucial for a company to keep track of its distributed data in such a heterogeneous environment. Cooperating departments need shared access to this data, to be able to increase their productivity. Multidatabases were introduced for this reason, in order to integrate data from heterogeneous sources. One of the main challenges in the integration of data in such environments is the autonomy of the participating data nodes. This autonomy implies the ability to choose its own database design and operational behavior. Local autonomy is tightly attached to the data ownership, i.e. who is responsible for the correctness, availability, and consistency of the shared data. Centralizing data means to limit local autonomy and revoke the responsibility from the local administrator, which is not reasonable in many cases. A federated architecture for decentralizing data has to balance both, the highest possible local autonomy and a reasonable degree of information sharing [6]. Hence, the architecture of a company wide information system has to be applicable to the data policy of the company and vice versa. To be more precise, the question of data ownership determines the composition of the company wide information platform, while it has to ensure a high level of consistency and fail-safety.In this paper we describe the DÍGAME architecture, a Dynamic Information Grid in an Active Multidatabase Environment, which connects heterogeneous and autonomous data sources to support loosely coupled intra-and inter-enterprise collaboration. We have enhanced the multidatabase architecture of Heimbigner and McLeod [6] with Peer-to-Peer (P2P) concepts to offer a flexible information grid with high data availability to provide each participating node o...
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