Information for "Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins"

With the ever-increasing amount of data on the Web available at SPARQL endpoints the need for an integrated and transparent way of accessing the data has arisen. It is highly desirable to have a way of asking SPARQL queries that make use of data residing in disparate data sources served by multiple SPARQL endpoints. We aim at providing such a capability and thus enabling an integrated way of querying the whole Semantic Web at a time.

We would like to further improve the query evaluation performance by introducing a distributed join-aware join reordering. We will make use of the current Sesame optimization techniques for local queries and add our own component which will be re-ordering joins according to their relative costs. The costs will be based on statistics taking into account a sub-query selectivity combined with the distinction whether a triple pattern is supposed to be evaluated locally or at a remote SPARQL endpoint.

In addition to join re-ordering we would like to make use of statistics about SPARQL endpoints in order to optimize queries even further. Hopefully the recent initiative called Vocabulary of Interlinked Datasets

(http://community.linkeddata.org/MediaWiki/index.php?VoiD) will get to a point where it could be used for this purpose.