Thèmes COM et SYM -Systèmes communicants et Systèmes symboliqueś Equipes-Projets Wam et Sardes Rapport de recherche n°6590 -Juillet 2008 -41 pagesAbstract: We present an algorithm to solve XPath decision problems under regular tree type constraints and show its use to statically type-check XPath queries. To this end, we prove the decidability of a logic with converse for finite ordered trees whose time complexity is a simple exponential of the size of a formula. The logic corresponds to the alternation free modal µ-calculus without greatest fixpoint, restricted to finite trees, and where formulas are cycle-free. Our proof method is based on two auxiliary results. First, XML regular tree types and XPath expressions have a linear translation to cycle-free formulas. Second, the least and greatest fixpoints are equivalent for finite trees, hence the logic is closed under negation.Building on these results, we describe a practical, effective system for solving the satisfiability of a formula. The system has been experimented with some decision problems such as XPath emptiness, containment, overlap, and coverage, with or without type constraints. The benefit of the approach is that our system can be effectively used in static analyzers for programming languages manipulating both XPath expressions and XML type annotations (as input and output types).Key-words: Mu-calculus, satisfiability, trees, XPath, queries, XML, types, regular tree grammars An extended abstract of this work was presented at the ACM Conference on Programming Language Design and Implementation (PLDI), 2007 [21]. Extensions included in this article notably comprise proof sketches, crucial implementation techniques for building a satisfiability-testing algorithm which performs well in practice, a detailed description of the algorithm, and formal descriptions and explanations about an important property of the logic: cycle-freeness for formulas.

sparql is the w3c standard query language for querying data expressed in the Resource Description Framework (rdf). The increasing amounts of rdf data available raise a major need and research interest in building efficient and scalable distributed sparql query evaluators. In this context, we propose sparqlgx: our implementation of a distributed rdf datastore based on Apache Spark. sparqlgx is designed to leverage existing Hadoop infrastructures for evaluating sparql queries. sparqlgx relies on a translation of sparql queries into executable Spark code that adopts evaluation strategies according to (1) the storage method used and (2) statistics on data. We show that sparqlgx makes it possible to evaluate sparql queries on billions of triples distributed across multiple nodes, while providing attractive performance figures. We report on experiments which show how sparqlgx compares to related state-of-the-art implementations and we show that our approach scales better than these systems in terms of supported dataset size. With its simple design, sparqlgx represents an interesting alternative in several scenarios.