1,576 research outputs found
Foundations of SPARQL Query Optimization
The SPARQL query language is a recent W3C standard for processing RDF data, a
format that has been developed to encode information in a machine-readable way.
We investigate the foundations of SPARQL query optimization and (a) provide
novel complexity results for the SPARQL evaluation problem, showing that the
main source of complexity is operator OPTIONAL alone; (b) propose a
comprehensive set of algebraic query rewriting rules; (c) present a framework
for constraint-based SPARQL optimization based upon the well-known chase
procedure for Conjunctive Query minimization. In this line, we develop two
novel termination conditions for the chase. They subsume the strongest
conditions known so far and do not increase the complexity of the recognition
problem, thus making a larger class of both Conjunctive and SPARQL queries
amenable to constraint-based optimization. Our results are of immediate
practical interest and might empower any SPARQL query optimizer
An extension of SPARQL for expressing qualitative preferences
In this paper we present SPREFQL, an extension of the SPARQL language that
allows appending a PREFER clause that expresses "soft" preferences over the
query results obtained by the main body of the query. The extension does not
add expressivity and any SPREFQL query can be transformed to an equivalent
standard SPARQL query. However, clearly separating preferences from the "hard"
patterns and filters in the WHERE clause gives queries where the intention of
the client is more cleanly expressed, an advantage for both human readability
and machine optimization. In the paper we formally define the syntax and the
semantics of the extension and we also provide empirical evidence that
optimizations specific to SPREFQL improve run-time efficiency by comparison to
the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna,
October 201
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
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