The Web of Data has grown enormously over the last years. Currently, it comprises a large compendium of interlinked and distributed datasets from multiple domains. The abundance of datasets has motivated considerable work for developing SPARQL query federation systems, the dedicated means to access data distributed over the Web of Data. However, the granularity of previous evaluations of such systems has not allowed deriving of insights concerning their behavior in different steps involved during federated query processing. In this work, we perform extensive experiments to compare state-of-the-art SPARQL endpoint federation systems using the comprehensive performance evaluation framework FedBench. We extend the scope of the performance evaluation by considering additional criteria to the commonly used key criterion (i.e. the query runtime). In particular, we consider the number of sources selected, total number of SPARQL ASK requests used, and source selection time, the criteria which have not received much attention in the previous studies. Yet, we show that they have a significant impact on the overall query runtime of existing systems. Also, we extend FedBench to mirror a highly distributed data environment and assess the behavior of existing systems by using the same four criteria. As the result we provide a detailed analysis of the experimental outcomes that reveal novel insights for improving current and future SPARQL federation systems.

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Abstract. This demo presents LODStats, a web application for collection and exploration of the Linked Open Data statistics. LODStats consists of two parts: the core collects statistics about the LOD cloud and publishes it on the LODStats web portal, a front-end for exploration of dataset statistics. Statistics are published both in human-readable and machine-readable formats, thus allowing consumption of the data through web front-end by the users as well as through an API by services and applications. As an example for the latter we showcase how to visualize the statistical data with the CubeViz application.

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