A complete translation from SPARQL into efficient SQL

Elliott, Brendan; Cheng, En; Thomas-Ogbuji, Chimezie; Özsoyoğlu, Z. Meral

doi:10.1145/1620432.1620437

Cited by 50 publications

(52 citation statements)

References 13 publications

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“…This algorithm is also generic with respect to the underlying SQL schema. Their evaluation was also on a triple-table style schema on small datasets of 2-5 million triples [30]. While in theory, these two SPARQL to SQL translations approaches should function as a RDB2RDF system on any SQL database, both their evaluations were limited to building a triplestore on top of a relational database.…”

Section: Related Workmentioning

confidence: 99%

Ultrawrap: SPARQL execution on relational data

Sequeda

Miranker

2013

Journal of Web Semantics

108

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Abstract:The Semantic Web's promise to achieve web-wide data integration requires the inclusion of legacy relational data as RDF, which, in turn, requires the execution of SPARQL queries on the legacy relational database. In this paper we explore a hypothesis: existing commercial relational databases already subsume the algorithms and optimizations needed to support effective SPARQL execution on existing relationally stored data. The experiment, embodied in a system called Ultrawrap, comprises encoding a logical representation of the database as a graph using SQL views and a simple syntactic translation of SPARQL queries to SQL queries on those views. Thus, in the course executing a SPARQL query, the SQL optimizer both instantiates a mapping of relational data to RDF and optimizes its execution. Other approaches typically implement aspects of query optimization and execution outside the SQL environment.Ultrawrap is evaluated using two benchmarks across the three major relational database management systems. We identify two important optimizations: detection of unsatisfiable conditions and self-join elimination, such that, when applied, SPARQL queries execute at nearly the same speed as semantically equivalent native SQL queries, providing strong evidence of the validity of the hypothesis.

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Section: Related Workmentioning

confidence: 99%

Ultrawrap: SPARQL execution on relational data

Sequeda

Miranker

2013

Journal of Web Semantics

108

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“…However, there are RDBMS that need special at tention (e.g., MySQL, PostgreSQL), because the resulting queries contain some properties that affect the capabilities of the database optimizer. For this reason, whenever it is possible, we remove non-correlated subqueries from the re sulting queries by pushing down projections and selections (as advocated by [10]). We also remove self-joins that occur in the rewritten queries such as when the graph patterns con tain alpha mappings coming from the same …”

Section: Query Rewriting Optimizationsmentioning

confidence: 99%

“…In the context of RDF data management, some works [7,10] have focused on using relational databases as the backend for triple stores, with the idea of exploiting the performance provided by relational database systems. They normally work with schema-oblivious layouts (using a triple table layout containing columns corresponding to the triple elements) or vertical partitioned layouts (using different tables to store triples corresponding to the same predicate).…”

Section: Introductionmentioning

confidence: 99%

Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph

Priyatna

Corcho

Sequeda

2014

Proceedings of the 23rd International Conference on World Wide Web

108

111

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R2RML is used to specify transformations of data avail able in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underly ing relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

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“…A number of translations of SPARQL queries (under simple entailment) to SQL queries have already been suggested in the literature; see, e.g., [9,13,7,32,27]. However, none of them is suitable for our aims because they do not take into account the three-valued logic used in the OPTIONAL and BOUND constructs of the current SPARQL 1.1 (the semantics of OPTIONAL was not compositional in SPARQL 1.0).…”

Section: Translating Sparql Under Simple Entailment To Sqlmentioning

confidence: 99%

Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime

Kontchakov

Rezk

Rodríguez-Muro

et al. 2014

Lecture Notes in Computer Science

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Abstract. We present an extension of the ontology-based data access platform Ontop that supports answering SPARQL queries under the OWL 2 QL direct semantics entailment regime for data instances stored in relational databases. On the theoretical side, we show how any input SPARQL query, OWL 2 QL ontology and R2RML mappings can be rewritten to an equivalent SQL query solely over the data. On the practical side, we present initial experimental results demonstrating that by applying the Ontop technologies-the tree-witness query rewriting, T -mappings compiling R2RML mappings with ontology hierarchies, and T -mapping optimisations using SQL expressivity and database integrity constraints-the system produces scalable SQL queries.

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A complete translation from SPARQL into efficient SQL

Cited by 50 publications

References 13 publications

Ultrawrap: SPARQL execution on relational data

Ultrawrap: SPARQL execution on relational data

Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph

Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime

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