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TriAL-QL: Distributed Processing of Navigational Queries

Published: 31 May 2015 Publication History

Abstract

Navigational queries are among the most natural query patterns for RDF data, but yet most existing RDF query languages fail to cover all the varieties inherent to its triple-based model, including SPARQL 1.1 and its derivatives. As a consequence, the development of more expressive RDF languages is of general interest. With TriAL* [14], there exists an expressive algebra which subsumes many previous approaches, while adding novel features that are not expressible in most other RDF query languages based on the standard graph model. However, its algebraic notation is inappropriate for practical usage and it is not supported by any existing RDF triple store. In this paper, we propose TriAL-QL, an easy to write and grasp language for TriAL*, preserving its compositional algebraic structure. We present an implementation based on Impala, a massive parallel SQL query engine on Hadoop, using an optimized semi-naive evaluation for the recursive fragments of TriAL*. This way, we support both data-intensive ETL-like workloads and explorative ad-hoc style queries. To demonstrate the scalability and expressiveness of our approach, we conducted experiments on generated social networks with up to 1.8 billion triples and compared different execution strategies to a Hive-based solution.

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Cited By

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  • (2018)Storing and Querying Semantic Data in the CloudReasoning Web. Learning, Uncertainty, Streaming, and Scalability10.1007/978-3-030-00338-8_7(173-222)Online publication date: 22-Sep-2018
  • (2017)Querying Semantic Knowledge Bases with SQL-on-HadoopProceedings of the 4th ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond10.1145/3070607.3070610(1-10)Online publication date: 14-May-2017

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cover image ACM Conferences
WebDB'15: Proceedings of the 18th International Workshop on Web and Databases
May 2015
75 pages
ISBN:9781450336277
DOI:10.1145/2767109
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 31 May 2015

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SIGMOD/PODS'15
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SIGMOD/PODS'15: International Conference on Management of Data
May 31 - June 4, 2015
VIC, Melbourne, Australia

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WebDB'15 Paper Acceptance Rate 9 of 31 submissions, 29%;
Overall Acceptance Rate 30 of 100 submissions, 30%

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Cited By

View all
  • (2018)Storing and Querying Semantic Data in the CloudReasoning Web. Learning, Uncertainty, Streaming, and Scalability10.1007/978-3-030-00338-8_7(173-222)Online publication date: 22-Sep-2018
  • (2017)Querying Semantic Knowledge Bases with SQL-on-HadoopProceedings of the 4th ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond10.1145/3070607.3070610(1-10)Online publication date: 14-May-2017

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