We present statistics on real world SPARQL queries that may be of interest for building SPARQL query processing engines and benchmarks. In particular, we analyze the syntactical structure of queries in a log of about 3 million queries, harvested from the DBPedia SPARQL endpoint. Although a sizable portion of the log is shown to consist of so-called conjunctive SPARQL queries, non-conjunctive queries that use SPARQL's union or optional operators are more than substantial. It is known, however, that query evaluation quickly becomes hard for queries including the non-conjunctive operators union or optional. We therefore drill deeper into the syntactical structure of the queries that are not conjunctive and show that in 50% of the cases, these queries satisfy certain structural restrictions that imply tractable evaluation in theory. We hope that the identification of these restrictions can aid in the future development of practical heuristics for processing non-conjunctive SPARQL queries.
The satisfiability problem for SPARQL patterns is undecidable in general, since SPARQL 1.0 can express the relational algebra. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound-constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is decidable by syntactic checks on bound variables and on the use of literals. Although the problem is shown to be NP-complete, it is experimentally shown that the checks can be implemented efficiently in practice. The paper also points out that satisfiability for the so-called 'well-designed' patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.
As an essential part of the W3C's semantic web stack and linked data initiative, RDF data management systems (also known as triplestores) have drawn a lot of research attention. The majority of these systems use value-based indexes (e.g., B + -trees) for physical storage, and ignore many of the structural aspects present in RDF graphs. Structural indexes, on the other hand, have been successfully applied in XML and semi-structured data management to exploit structural graph information in query processing. In those settings, a structural index groups nodes in a graph based on some equivalence criterion, for example, indistinguishability with respect to some query workload (usually XPath). Motivated by this body of work, we have started the SAINT-DB project to study and develop a native RDF management system based on structural indexes. In this paper we present a principled framework for designing and using RDF structural indexes for practical fragments of SPARQL, based on recent formal structural characterizations of these fragments. We then explain how structural indexes can be incorporated in a typical query processing workflow; and discuss the design, implementation, and initial empirical evaluation of our approach. 1
In this chapter we present a general survey of the current state of the art in RDF storage and indexing. In the flurry of research on RDF data management in the last decade, we can identify three different perspectives on RDF: (1) a relational perspective; (2) an entity perspective; and (3) a graph-based perspective. Each of these three perspectives has drawn from ideas and results in three distinct research communities to propose solutions for managing RDF data: relational databases (for the relational perspective); information retrieval (for the entity perspective); and graph theory and graph databases (for the graph-based perspective). Our goal in this chapter is to give an up-to-date overview of represpentative solutions within each perspective.
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