ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints

Abstract. Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-then-execute paradigm, may timeout as a consequence of endpoint availability. Sec-ond, because blocking operators are usually implemented, endpoint query en-gines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availabil-ity and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traffic is bursty, and opportunis-tically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.

Distributed RDFS Reasoning Over Structured Overlay Networks

In this paper, we study the problem of distributed RDFS reasoning over structured overlay networks. Distributed RDFS reasoning is essential for providing the functionality that Semantic Web and Linked Data applications require. Our goal is to present various inference techniques for RDFS reasoning in a distributed environment, and analyze them both theoretically and experimentally. The reasoning methods we present are based on bottom-up and top-down techniques and have been implemented on top of the distributed hash table Bamboo. Our algorithms range from forward and backward chaining ones to rewriting algorithms based on magic sets. We formally prove the correctness of the algorithms and study the time-space trade-off they exhibit analytically and experimentally in a local cluster. © 2013, Springer-Verlag Berlin Heidelberg

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SPARQL query optimization on top of DHTs

We study the problem of SPARQL query optimization on top of distributed hash tables. Existing works on SPARQL query processing in such environments have never been implemented in a real system, or do not utilize any optimization techniques and thus exhibit poor performance. Our goal in this paper is to propose efficient and scalable algorithms for optimizing SPARQL basic graph pattern queries. We augment a known distributed query processing algorithm with query optimization strategies that improve performance in terms of query response time and bandwidth usage. We implement our techniques in the system Atlas and study their performance experimentally in a local cluster. © 2010 Springer-Verlag

RDFS reasoning and query answering on top of DHTs

We study the problem of distributed RDFS reasoning and query answering on top of distributed hash tables. Scalable, distributed RDFS reasoning is an essential functionality for providing the scalability and performance that large-scale Semantic Web applications require. Our goal in this paper is to compare and evaluate two well-known approaches to RDFS reasoning, namely backward and forward chaining, on top of distributed hash tables. We show how to implement both algorithms on top of the distributed hash table Bamboo and prove their correctness. We also study the time-space trade-off exhibited by the algorithms analytically, and experimentally by evaluating our algorithms on PlanetLab. © 2008 Springer Berlin Heidelberg

D2R2: Disk-oriented Deductive Reasoning in a {RISC-style} {RDF} Engine

Abstract. Deductive reasoning lies in the expressive intersection of Datalog and Description Logics. In this paper, we present the D2R2 engine, which implements deductive reasoning capabilities based on the Query-Sub-Query (QSQR) algorithm on top of the disk-oriented RDF-3X engine. D2R2 aims to bridge the gap between rule-oriented (inten-sional) reasoning with deduction rules and data-oriented (extensional) processing of large joins, over a set of highly tuned, disk-based index structures for large RDF collections. We present a generalization of QSQR, which allows for dynamic sub-query scheduling and chaining of extensional predicates into atomic join patterns—two key extensions for coupling QSQR with a disk-oriented storage backend. Experiments over a set of recursive queries and a very large knowledge base, consisting of 20 million RDF facts, as well as comparisons to disk-oriented reasoning engines, confirm the practical viability and significant runtime improve-ments of D2R2 compared to these engines