FedX: Optimization Techniques for Federated Query Processing on Linked Data

author: Andreas Schwarte, fluid Operations AG
published: Nov. 25, 2011,   recorded: October 2011,   views: 3183


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Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on theWeb, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an e ffective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a signifi cant improvement in query performance over state-of-the-art federated query engines.

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