Abstract. In this contribution a system is presented, which provides access to distributed data sources using Semantic Web technology. While it was primarily designed for data sharing and scientific collaboration, it is regarded as a base technology useful for many other Semantic Web applications. The proposed system allows to retrieve data using SPARQL queries, data sources can register and abandon freely, and all RDF Schema or OWL vocabularies can be used to describe their data, as long as they are accessible on the Web. Data heterogeneity is addressed by RDF-wrappers like D2R-Server placed on top of local information systems. A query does not directly refer to actual endpoints, instead it contains graph patterns adhering to a virtual data set. A mediator finally pulls and joins RDF data from different endpoints providing a transparent on-the-fly view to the end-user.The SPARQL protocol has been defined to enable systematic data access to remote endpoints. However, remote SPARQL queries require the explicit notion of endpoint URIs. The presented system allows users to execute queries without the need to specify target endpoints. Additionally, it is possible to execute join and union operations across different remote endpoints. The optimization of such distributed operations is a key factor concerning the performance of the overall system. Therefore, proven concepts from database research can be applied.

Abstract. In this paper a novel approach is presented for generating RDF graphs of arbitrary complexity from various spreadsheet layouts. Currently, none of the available spreadsheet-to-RDF wrappers supports cross tables and tables where data is not aligned in rows. Similar to RDF123, XLWrap is based on template graphs where fragments of triples can be mapped to specific cells of a spreadsheet. Additionally, it features a full expression algebra based on the syntax of OpenOffice Calc and various shift operations, which can be used to repeat similar mappings in order to wrap cross tables including multiple sheets and spreadsheet files. The set of available expression functions includes most of the native functions of OpenOffice Calc and can be easily extended by users of XLWrap.Additionally, XLWrap is able to execute SPARQL queries, and since it is possible to define multiple virtual class extents in a mapping specification, it can be used to integrate information from multiple spreadsheets. XLWrap supports a special identity concept which allows to link anonymous resources (blank nodes) -which may originate from different spreadsheets -in the target graph.

In this paper RDFStats is introduced, which is a generator for statistics of RDF sources like SPARQL endpoints and RDF documents. RDFStats does not only provide a statistics generator, but also a powerful API for persisting and accessing statistics including several estimation functions that also support SPARQL filter-like expressions.For many Semantic Web applications like the Semantic Web Integrator and Query Engine (SemWIQ), which is currently developed at the University of Linz, detailed statistics about the contents of RDF data sources are very important. RDFStats has been primarily designed and implemented for the SemWIQ federator and optimizer, but it can also be used for other applications like linked data browsers, aggregators, or visualization tools. It is based on the popular Semantic Web framework Jena developed by HP Labs Bristol and can be easily extended and integrated into other applications.

During recent years an increasing number of data providers adopted the Linked Data principles for publishing and connecting structured data on the Web, thus creating a globally distributed dataspacethe Web of Data. While the execution of structured, SQL-like queries over this dataspace opens possibilities not conceivable before, query execution on the Web of Data poses novel challenges. These challenges provide great opportunities for the database community.In this article we introduce the concept of Linked Data and discuss different approaches to query the Web of Data. Our goal is to provide a general understanding of this new research area and of the challenges and open issues that must be addressed.

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