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Gauging triple stores with actual biological data

(2012) BMC BIOINFORMATICS. 13(suppl. 1).
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Abstract
Background: Semantic Web technologies have been developed to overcome the limitations of the current Web and conventional data integration solutions. The Semantic Web is expected to link all the data present on the Internet instead of linking just documents. One of the foundations of the Semantic Web technologies is the knowledge representation language Resource Description Framework (RDF). Knowledge expressed in RDF is typically stored in so-called triple stores (also known as RDF stores), from which it can be retrieved with SPARQL, a language designed for querying RDF-based models. The Semantic Web technologies should allow federated queries over multiple triple stores. In this paper we compare the efficiency of a set of biologically relevant queries as applied to a number of different triple store implementations. Results: Previously we developed a library of queries to guide the use of our knowledge base Cell Cycle Ontology implemented as a triple store. We have now compared the performance of these queries on five non-commercial triple stores: OpenLink Virtuoso (Open-Source Edition), Jena SDB, Jena TDB, SwiftOWLIM and 4Store. We examined three performance aspects: the data uploading time, the query execution time and the scalability. The queries we had chosen addressed diverse ontological or biological questions, and we found that individual store performance was quite query-specific. We identified three groups of queries displaying similar behaviour across the different stores: 1) relatively short response time queries, 2) moderate response time queries and 3) relatively long response time queries. SwiftOWLIM proved to be a winner in the first group, 4Store in the second one and Virtuoso in the third one. Conclusions: Our analysis showed that some queries behaved idiosyncratically, in a triple store specific manner, mainly with SwiftOWLIM and 4Store. Virtuoso, as expected, displayed a very balanced performance - its load time and its response time for all the tested queries were better than average among the selected stores; it showed a very good scalability and a reasonable run-to-run reproducibility. Jena SDB and Jena TDB were consistently slower than the other three implementations. Our analysis demonstrated that most queries developed for Virtuoso could be successfully used for other implementations.
Keywords
SYSTEMS, SEMANTIC WEB

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Chicago
Mironov, Vladimir, Nirmala Seethappan, Ward Blondé, Erick Zimar Antezana San Roman, Andrea Splendiani, and Martin Kuiper. 2012. “Gauging Triple Stores with Actual Biological Data.” Bmc Bioinformatics 13 (suppl. 1).
APA
Mironov, V., Seethappan, N., Blondé, W., Antezana San Roman, E. Z., Splendiani, A., & Kuiper, M. (2012). Gauging triple stores with actual biological data. BMC BIOINFORMATICS, 13(suppl. 1).
Vancouver
1.
Mironov V, Seethappan N, Blondé W, Antezana San Roman EZ, Splendiani A, Kuiper M. Gauging triple stores with actual biological data. BMC BIOINFORMATICS. 2012;13(suppl. 1).
MLA
Mironov, Vladimir, Nirmala Seethappan, Ward Blondé, et al. “Gauging Triple Stores with Actual Biological Data.” BMC BIOINFORMATICS 13.suppl. 1 (2012): n. pag. Print.
@article{3072981,
  abstract     = {Background: Semantic Web technologies have been developed to overcome the limitations of the current Web and conventional data integration solutions. The Semantic Web is expected to link all the data present on the Internet instead of linking just documents. One of the foundations of the Semantic Web technologies is the knowledge representation language Resource Description Framework (RDF). Knowledge expressed in RDF is typically stored in so-called triple stores (also known as RDF stores), from which it can be retrieved with SPARQL, a language designed for querying RDF-based models. The Semantic Web technologies should allow federated queries over multiple triple stores. In this paper we compare the efficiency of a set of biologically relevant queries as applied to a number of different triple store implementations. 
Results: Previously we developed a library of queries to guide the use of our knowledge base Cell Cycle Ontology implemented as a triple store. We have now compared the performance of these queries on five non-commercial triple stores: OpenLink Virtuoso (Open-Source Edition), Jena SDB, Jena TDB, SwiftOWLIM and 4Store. We examined three performance aspects: the data uploading time, the query execution time and the scalability. The queries we had chosen addressed diverse ontological or biological questions, and we found that individual store performance was quite query-specific. We identified three groups of queries displaying similar behaviour across the different stores: 1) relatively short response time queries, 2) moderate response time queries and 3) relatively long response time queries. SwiftOWLIM proved to be a winner in the first group, 4Store in the second one and Virtuoso in the third one. 
Conclusions: Our analysis showed that some queries behaved idiosyncratically, in a triple store specific manner, mainly with SwiftOWLIM and 4Store. Virtuoso, as expected, displayed a very balanced performance - its load time and its response time for all the tested queries were better than average among the selected stores; it showed a very good scalability and a reasonable run-to-run reproducibility. Jena SDB and Jena TDB were consistently slower than the other three implementations. Our analysis demonstrated that most queries developed for Virtuoso could be successfully used for other implementations.},
  articleno    = {S3},
  author       = {Mironov, Vladimir and Seethappan, Nirmala and Blond{\'e}, Ward and Antezana San Roman, Erick Zimar and Splendiani, Andrea and Kuiper, Martin},
  issn         = {1471-2105},
  journal      = {BMC BIOINFORMATICS},
  language     = {eng},
  number       = {suppl. 1},
  pages        = {8},
  title        = {Gauging triple stores with actual biological data},
  url          = {http://dx.doi.org/10.1186/1471-2105-13-S1-S3},
  volume       = {13},
  year         = {2012},
}

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