Heuristics-based query optimisation for SPARQL

Tsialiamanis, Petros; Sidirourgos, Lefteris; Fundulaki, Irini; Christophides, Vassilis; Boncz, Peter

doi:10.1145/2247596.2247635

Cited by 71 publications

(75 citation statements)

References 25 publications

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“…Although there is a large body of work in query optimization (both in SPARQL [8,11,13,17,19] and beyond), there are still important challenges in terms of (a) SPARQL query optimization, and (b) translation of SPARQL to equivalent SQL queries. Typical approaches perform bottom-up SPARQL query optimization, i.e., individual triples [17] or conjunctive SPARQL patterns [13] are independently optimized, and then the optimizer orders and merges these individual plans into one global plan.…”

Section: Introductionmentioning

confidence: 99%

Building an efficient RDF store over a relational database

Bornea

Dolby

Kementsietsidis

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

170

129

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Efficient storage and querying of RDF data is of increasing importance, due to the increased popularity and widespread acceptance of RDF on the web and in the enterprise. In this paper, we describe a novel storage and query mechanism for RDF which works on top of existing relational representations. Reliance on relational representations of RDF means that one can take advantage of 35+ years of research on efficient storage and querying, industrial-strength transaction support, locking, security, etc. However, there are significant challenges in storing RDF in relational, which include data sparsity and schema variability. We describe novel mechanisms to shred RDF into relational, and novel query translation techniques to maximize the advantages of this shredded representation. We show that these mechanisms result in consistently good performance across multiple RDF benchmarks, even when compared with current state-of-the-art stores. This work provides the basis for RDF support in DB2 v.10.1.

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Section: Introductionmentioning

confidence: 99%

Building an efficient RDF store over a relational database

Bornea

Dolby

Kementsietsidis

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

170

129

View full text Add to dashboard Cite

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“…Both queries are structurally identical (i.e., both are path-shaped and have the same number and type of triple patterns). Thus, both queries appear to be similarly selective [14]. However, when we execute them (under c Match -bag-semantics; cf.…”

Section: Introductionmentioning

confidence: 94%

Reachable subwebs for traversal-based query execution

Hartig

Özsu

2014

Proceedings of the 23rd International Conference on World Wide Web

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Traversal-based approaches to execute queries over data on the Web have recently been studied. These approaches make use of up-todate data from initially unknown data sources and, thus, enable applications to tap the full potential of the Web. While existing work focuses primarily on implementation techniques, a principled analysis of subwebs that are reachable by such approaches is missing. Such an analysis may help to gain new insight into the problem of optimizing the response time of traversal-based query engines. Furthermore, a better understanding of characteristics of such subwebs may also inform approaches to benchmark these engines.This paper provides such an analysis. In particular, we identify typical graph-based properties of query-specific reachable subwebs and quantify their diversity. Furthermore, we investigate whether vertex scoring methods (e.g., PageRank) are able to predict queryrelevance of data sources when applied to such subwebs.

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“…It has been recently argued [17] that SPARQL can be optimized just as well or even better without a cost model. We do not agree with this due to the following: It is true that a cost model has many complexities and possibilities for error.…”

Section: Query Optimizationmentioning

confidence: 99%

Advances in Large-Scale RDF Data Management

Boncz

Erling

Pham

2014

Linked Open Data -- Creating Knowledge Out of Interlinked Data

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Abstract.One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. Virtuoso has been chosen as the basic RDF store for the LOD2 project, and during the project it has been significantly improved by incorporating advanced relational database techniques from MonetDB and Vectorwise, turning it into a compressed column store with vectored execution. This has reduced the performance gap ("RDF tax") between Virtuoso's SQL and SPARQL query performance in a way that still respects the "schema-last" nature of RDF. However, by lacking schema information, RDF database systems such as Virtuoso still cannot use advanced relational storage optimizations such as table partitioning or clustered indexes and have to execute SPARQL queries with many selfjoins to a triple table, which leads to more join effort than needed in SQL systems. In this chapter, we first discuss the new column store techniques applied to Virtuoso, the enhancements in its cluster parallel version, and show its performance using the popular BSBM benchmark at the unsurpassed scale of 150 billion triples. We finally describe ongoing work in deriving an "emergent" relational schema from RDF data, which can help to close the performance gap between relational-based and RDF-based storage solutions. General ObjectivesOne of the objectives of the LOD2 EU project is to boost the performance and the scalability of RDF storage solutions so that it can, efficiently manage huge datasets of Linked Open Data (LOD). However, it has been noted that given similar data management tasks, relational database technology significantly outperformed RDF data stores. One controlled scenario in which the two technologies can be compared is the BSBM benchmark [2], which exists equivalent relational and RDF variants. As illustrated in Fig. 1, while the SQL systems can process by up to 40-175K QMpH, the Triple stores can only reach 1-10K QMpH, showing a factor of 15-40 of performances difference.

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Heuristics-based query optimisation for SPARQL

Cited by 71 publications

References 25 publications

Building an efficient RDF store over a relational database

Building an efficient RDF store over a relational database

Reachable subwebs for traversal-based query execution

Advances in Large-Scale RDF Data Management

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