The Odyssey Approach for Optimizing Federated SPARQL Queries

Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is only limited access to statistics about schema and instance data of remote sources. To overcome these challenges, most federated query engines rely on heuristics to reduce the space of possible query execution plans or on dynamic programming strategies to produce optimal plans. Nevertheless, these plans may still exhibit a high number of intermediate results or high execution times because of heuristics and inaccurate cost estimations. In this paper, we present Odyssey, an approach that uses statistics that allow for a more accurate cost estimation for federated queries and therefore enables Odyssey to produce better query execution plans. Our experimental results show that Odyssey produces query execution plans that are better in terms of data transfer and execution time than state-of-the-art optimizers. Our experiments using the FedBench benchmark show execution time gains of at least 25 times on average.Comment: 16 pages, 10 figure

Challenges of source selection in the WoD

Federated querying, the idea to execute queries over several distributed knowledge bases, lies at the core of the semantic web vision. To accommodate this vision, SPARQL provides the SERVICE keyword that allows one to allocate sub-queries to servers. In many cases, however, data may be available from multiple sources resulting in a combinatorially growing number of alternative allocations of subqueries to sources. Running a federated query on all possible sources might not be very lucrative from a user's point of view if extensive execution times or fees are involved in accessing the sources' data. To address this shortcoming, federated join-cardinality approximation techniques have been proposed to narrow down the number of possible allocations to a few most promising (or results-yielding) ones. In this paper, we analyze the usefulness of cardinality approximation for source selection. We compare both the runtime and accuracy of Bloom Filters empirically and elaborate on their suitability and limitations for different kind of queries. As we show, the performance of cardinality approximations of federated SPARQL queries degenerates when applied to queries with multiple joins of low selectivity. We generalize our results analytically to any estimation technique exhibiting false positives. These findings argue for a renewed effort to find novel join-cardinality approximation techniques or a change of paradigm in query execution to settings, where such estimations play a less important role