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A Transformation of the RDF Mapping Language into a High-Level Data Analysis Language for Execution in a Distributed Computing Environment

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1427))

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Abstract

Nowadays scientific data should be FAIR that are Findable, Accessible, Interoperable and Reusable. Reference implementation of FAIR data management principles proposed recently considers RDF as unifying data model and RDF Mapping Language (RML) as the basic language for data integration. This paper is aimed at development of methods and tools for scalable data integration in the frame of this architecture. A mapping from RML into a high-level data analysis language Pig Latin that runs on Hadoop is considered. The mapping is implemented using model transformation technologies. These allows to execute RML programs in the Hadoop distributed computing environment. According to the experimental evaluation RML implementation developed scales w.r.t. data volume and outperforms related implementations.

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Notes

  1. 1.

    https://www.w3.org/XML/Schema.

  2. 2.

    https://github.com/RMLio/rmlmapper-java.

  3. 3.

    https://github.com/RMLio/RMLStreamer.

  4. 4.

    https://flink.apache.org/.

  5. 5.

    https://hadoop.apache.org/.

  6. 6.

    https://www.datanyze.com/market-share/big-data-processing--204?page=1.

  7. 7.

    https://www.eclipse.org/Xtext/.

  8. 8.

    https://www.eclipse.org/atl/.

  9. 9.

    https://github.com/antidot/db2triples.

  10. 10.

    https://www.w3.org/RDF/.

  11. 11.

    https://www.w3.org/TR/turtle/.

  12. 12.

    https://github.com/tangwwwfei/RML2Pig/tree/master/LoadTurtle/src/main/java/r2ps/udf/pig/R2PFORMAT.java.

  13. 13.

    RML2Pig Project, https://github.com/tangwwwfei/RML2Pig.

  14. 14.

    https://www.eclipse.org/Xtext/.

  15. 15.

    https://www.w3.org/TR/turtle/.

  16. 16.

    https://www.w3.org/TR/turtle/#sec-grammar.

  17. 17.

    https://github.com/tangwwwfei/RML2Pig/tree/master/org.xtext.r2ps.rml/model/generated/RML.ecore.

  18. 18.

    https://github.com/tangwwwfei/RML2Pig/tree/master/RML2Pig/metamodels/RML.ecore.

  19. 19.

    https://rml.io/specs/rml/.

  20. 20.

    https://github.com/tangwwwfei/RML2Pig/tree/master/org.xtext.r2ps.rml/src/org/xtext/r2ps/rml/generator.

  21. 21.

    https://www.eclipse.org/xtend/.

  22. 22.

    https://github.com/tangwwwfei/RML2Pig/tree/master/RML2Pig/metamodels/Pig.ecore.

  23. 23.

    https://www.eclipse.org/atl/.

  24. 24.

    https://figshare.com/articles/Data_for_bounded_data_study/6115049.

  25. 25.

    https://github.com/tangwwwfei/RML2Pig/tree/master/test-cases/person.py.

  26. 26.

    https://github.com/joke2k/faker.

  27. 27.

    https://github.com/tangwwwfei/RML2Pig/tree/master/test-cases.

  28. 28.

    https://github.com/tangwwwfei/RML2Pig/tree/master/TestEnvironment/TestScripts.

  29. 29.

    https://github.com/RMLio/RMLStreamer/tree/master/docker.

  30. 30.

    https://github.com/tangwwwfei/RML2Pig/tree/master/TestEnvironment/docker-hadoop.

  31. 31.

    https://github.com/tangwwwfei/RML2Pig/tree/master/TestEnvironment/docker-spark-yarn-cluster.

  32. 32.

    https://github.com/RMLio/rmlmapper-java.

  33. 33.

    https://github.com/tangwwwfei/RML2Pig/tree/master/RML2Scala.

  34. 34.

    https://www.w3.org/TR/xpath-10/.

  35. 35.

    https://github.com/tangwwwfei/RML2Pig/tree/master/test-cases/resources/passed/test-cases.

  36. 36.

    https://www.w3.org/TR/rdb2rdf-test-cases/.

  37. 37.

    https://github.com/RMLio/rmlmapper-java/tree/master/src/test/resources/test-cases.

  38. 38.

    Among data retrieval features described in RML documentation JSON, CSV and XML data files from HDFS, RDF data from SPARQL endpoints, relational data from several DBMSs are supported. CSVW (CSV on the Web Vocabulary) and D2RQ Mapping Language are only partially supported. DCAT (Data Catalog Vocabulary) and Hydra core vocabulary for Web API description are not supported.

  39. 39.

    https://www.w3.org/TR/n-quads/.

References

  1. Anastasia, D., Miel, Vander, S.: RML specification (2014). http://rml.io/spec.html

  2. Bettini, L.: Implementing Domain-Specific Languages with Xtext and Xtend. Packt Publishing Ltd. (2016)

    Google Scholar 

  3. Dimou, A.: High quality linked data generation from heterogeneous data. Ph.D. thesis, University of Antwerp (2017)

    Google Scholar 

  4. Dimou, A., Sande, M.V., Colpaert, P., Verborgh, R., Mannens, E., de Walle, R.V.: RML: a generic language for integrated rdf mappings of heterogeneous data. In: Bizer, C., Heath, T., Auer, S., Berners-Lee, T. (eds.) Proceedings of the Workshop on Linked Data on the Web co-located with the 23rd International World Wide Web Conference (WWW 2014), Seoul, Korea, 8 April 2014. CEUR Workshop Proceedings, vol. 1184. CEUR-WS.org (2014). http://ceur-ws.org/Vol-1184/ldow2014_paper_01.pdf

  5. Dürst, M., Suignard, M.: Internationalized resource identifiers (IRIs). Proposed standard RFC 3987, Network Working Group (2005)

    Google Scholar 

  6. Klyne, G., Carroll, J.J., McBride, B.: RDF 1.1 concepts and abstract syntax. Recommendation, W3C (2014). https://www.w3.org/TR/rdf11-concepts/

  7. Michel, F., Montagnat, J., Zucker, C.F.: A survey of RDB to RDF translation approaches and tools. Research Report hal-00903568 (2014)

    Google Scholar 

  8. Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. pp. 1099–1110. ACM (2008)

    Google Scholar 

  9. Sequeda, J.F., Miranker, D.P.: Ultrawrap mapper: a semi-automatic relational database to RDF (RDB2RDF) mapping tool. In: Villata, S., Pan, J.Z., Dragoni, M. (eds.) Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015), Bethlehem, PA, USA, 11 October 2015. CEUR Workshop Proceedings, vol. 1486. CEUR-WS.org (2015). http://ceur-ws.org/Vol-1486/paper_105.pdf

  10. Speicher, S., Arwe, J., Malhotra, A.: Linked data platform 1.0. W3C recommendation, W3C, February 2015. http://www.w3.org/TR/2015/REC-ldp-20150226/

  11. Sundara, S., Das, S., Cyganiak, R.: R2RML: RDB to RDF mapping language. W3C recommendation, W3C, September 2012. http://www.w3.org/TR/2012/REC-r2rml-20120927/

  12. Verborgh, R., et al.: Triple pattern fragments: a low-cost knowledge graph interface for the web. J. Web Semant. 37, 184–206 (2016)

    Article  Google Scholar 

  13. Wilkinson, M.D., et al.: The fair guiding principles for scientific data management and stewardship. Sci. Data 3 (2016). https://doi.org/10.1038/sdata.2016.18

  14. Wilkinson, M.D., et al.: Interoperability and fairness through a novel combination of web technologies. PeerJ Comput. Sci. 3, e110 (2017)

    Article  Google Scholar 

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Acknowledgement

The research is financially supported by Russian Foundation for Basic Research, projects 18-07-01434, 18-29-22096. The research was carried out using infrastructure of shared research facilities CKP “Informatics” (http://www.frccsc.ru/ckp) of FRC CSC RAS.

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, GSP-1, Leninskiye Gory 1-52, 119991, Moscow, Russia

    Wenfei Tang

  2. Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova st. 44-2, 119333, Moscow, Russia

    Sergey Stupnikov

Authors
  1. Wenfei Tang
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Correspondence to Sergey Stupnikov .

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Editors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    Alexander Sychev

  2. Voronezh State University, Voronezh, Russia

    Sergey Makhortov

  3. Christian-Albrecht University of Kiel, Kiel, Schleswig-Holstein, Germany

    Bernhard Thalheim

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Tang, W., Stupnikov, S. (2021). A Transformation of the RDF Mapping Language into a High-Level Data Analysis Language for Execution in a Distributed Computing Environment. In: Sychev, A., Makhortov, S., Thalheim, B. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2020. Communications in Computer and Information Science, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-030-81200-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-81200-3_6

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