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      A Stitch in Time Saves Nine -- SPARQL querying of Property Graphs using Gremlin Traversals


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          Knowledge graphs have become popular over the past decade and frequently rely on the Resource Description Framework (RDF) or Property Graph (PG) databases as data models. However, the query languages for these two data models -- SPARQL for RDF and the property graph traversal language Gremlin -- are lacking interoperability. We present Gremlinator, a novel SPARQL to Gremlin translator. Gremlinator translates SPARQL queries to Gremlin traversals for executing graph pattern matching queries over graph databases. This allows to access and query a wide variety of Graph Data Management Systems (DMSs) using the W3C standardized SPARQL and avoid the steep learning curve of a new Graph Query Language (GQL). Gremlin is a graph computing system agnostic traversal language (covering both OLTP graph database or OLAP graph processors), making it a desirable choice for supporting interoperability for querying Graph DMSs. We present an empirical evaluation for the validity of our approach by formalizing the graph pattern matching construct of Gremlin and illustrate its mapping to corresponding SPARQL queries. Moreover, we also present a proof-of-concept implementation of our approach, demonstrate its validity and applicability by executing SPARQL queries on top of leading Graph stores (Neo4J, Sparksee, and Apache TinkerGraph) and compare their performances with RDF stores (Openlink Virtuoso, 4Store, and JenaTDB). The results indicate that, for complex queries (such as Star-shaped), Gremlin pattern matching traversals out-perform their corresponding SPARQL queries significantly, including their translation time. Gremlinator currently covers a subset of the SPARQL 1.0 specification, specifically the SELECT queries.

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          Most cited references 14

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          Foundations of SPARQL query optimization

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            The Expressive Power of SPARQL

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              Efficient SPARQL-to-SQL with R2RML mappings


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                09 January 2018


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