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Document-oriented RDF graph store

Identyfikatory
Warianty tytułu
PL
Dokumentowy magazyn grafów RDF
Języki publikacji
EN
Abstrakty
EN
Document-oriented NoSQL databases are not commonly used in Se-mantic Web and Linked Data environments. The article describes the idea of an document-oriented RDF graph store. We present alternative RDF serialisation, allowing for efficient processing of graph data in an NOSQL graph store. This means that a da-tabase such as RethinkDB can be an RDF graph store. Moreover, our proposal sup-ports various techniques for caching, which is a novelty for an RDF/JSON serialization.
PL
Dokumentowe bazy danych NoSQL nie są powszechnie używane w środowiskach Semantycznego Internetu i Danych Połączonych. Artykuł omawia propozycję dokumentowego magazynu grafów RDF. Przedstawiamy alternatywną serializację RDF, pozwalającą na wydajne przetwarzanie danych grafowych w bazie danych NOSQL. Oznacza to, że baza danych taka, jak RethinkDB może być magazynem grafów RDF. Co więcej, nasza propozycja obsługuje różne techniki buforowania, co jest nowością dla serializacji RDF/JSON.
Czasopismo
Rocznik
Strony
31--43
Opis fizyczny
Bibliogr. 33 poz.
Twórcy
  • University of Bialystok, Institute of Informatics, ul. Konstantego Ciołkowskiego 1M, 15-245 Bialystok, Poland
autor
  • University of Bialystok, Institute of Informatics, ul. Konstantego Ciołkowskiego 1M, 15-245 Bialystok, Poland
Bibliografia
  • 1. Aranda-Andújar A., Bugiotti F., Camacho-Rodríguez J., Colazzo D., Goasdoué F., Kaoudi Z., Manolescu I.: AMADA: web data repositories in the Amazon Cloud. Pro-ceedings of the 21st ACM international conference on Information and knowledge man-agement, ACM, 2012, p. 2749÷2751.
  • 2. Bizer C., Heath T., Berners-Lee T.: Linked data-the story so far. 2009.
  • 3. Bizer C., Schultz A.: The Berlin SPARQL benchmark. 2009.
  • 4. Broekstra J., Kampman A., van Harmelen F.: Sesame: A generic architecture for storing and querying RDF and RDF schema. In The Semantic Web – ISWC 2002, Springer, 2002, p. 54÷68.
  • 5. Bugiotti F., Goasdoué F., Kaoudi Z., Manolescu I.: RDF data management in the Ama-zon Cloud. Proceedings of the 2012 Joint EDBT/ICDT Workshops, ACM, 2012, p. 61÷72.
  • 6. Cudré-Mauroux F., Enchev I., Fundatureanu S., Groth P., Haque A., Harth A., Keppmann F.L., Miranker D., Sequeda J.F., Wylot M.: NoSQL databases for RDF: An empirical evaluation. In The Semantic Web – ISWC 2013, Springer, 2013, p. 310÷325.
  • 7. Cuzzocrea A., Cosulschi M., de Virgilio R.: An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs. Algo-rithms, Vol. 9(1), 2016, p. 7.
  • 8. Cyganiak R., Wood D., Lanthaler M.: RDF 1.1 Concepts and Abstract Syntax. W3C recommendation, World Wide Web Consortium, February 2014, http://www.w3.org/ TR/2014/REC-rdf11-concepts-20140225/.
  • 9. Dean J., Ghemawat S.: MapReduce: simplified data processing on large clusters. Com-munications of the ACM, Vol. 51(1), 2008, p. 107÷113.
  • 10. Dohmen L., Edlich I.S., Hackstein M.: A Declarative Web Framework for the Server-side Extension of the Multi Model Database ArangoDB. 2014.
  • 11. Fielding R., Nottingham M., Reschke J.: Hypertext Transfer Protocol (HTTP/1.1): Caching. Technical Report 7234, RFC Editor, June 2014, http://www.rfc-editor.org/ rfc/rfc7234.txt.
  • 12. Galárraga L., Hose K., Schenkel R.: Partout: A distributed engine for efficient RDF processing. Proceedings of the companion publication of the 23rd international confer-ence on World Wide Web companion, International World Wide Web Conferences Steering Committee, 2014, p. 267÷268.
  • 13. Hose K., Schenkel R.: WARP: Workload-aware replication and partitioning for RDF. 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), IEEE, 2013, p. 1÷6.
  • 14. Huang J., Abadi D.J., Ren K.: Scalable SPARQL querying of large RDF graphs. Pro-ceedings of the VLDB Endowment, Vol. 4(11), 2011, p. 1123÷1134.
  • 15. Kaoudi Z., Manolescu I.: RDF in the Clouds: A Survey. The VLDB Journal, 2014, p. 1÷25.
  • 16. Khadilkar V., Kantarcioglu M., Thuraisingham B., Castagna P.: Jena-hbase: A distrib-uted, scalable and efficient RDF triple store. Proceedings of the 11th International Se-mantic Web Conference Posters & Demonstrations Track, ISWC-PD, Vol. 12, Citeseer, 2012, p. 85÷88.
  • 17. Ladwig G., Harth A.: CumulusRDF: Linked Data Management on Nested Key-Value Stores. Proceedings of the 7th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS2011) at the 10th International Semantic Web Confer-ence (ISWC2011), 2011.
  • 18. McBride B.: Jena: A semantic web toolkit. IEEE Internet computing, Vol. 6(6), 2002, p. 55÷59.
  • 19. Papailiou N., Konstantinou I., Tsoumakos D., Koziris N.: H2RDF: adaptive query proc-essing on RDF data in the cloud. Proceedings of the 21st international conference com-panion on World Wide Web, ACM, 2012, p. 397÷400.
  • 20. Podlipnig S., Böszörmenyi L.: A survey of web cache replacement strategies. ACM Computing Surveys (CSUR), Vol. 35(4), 2003, p. 374÷398.
  • 21. Przyjaciel-Zablocki M., Schätzle A., Hornung T., Dorner C., Lausen G.: Cascading map-side joins over HBase for scalable join processing. CoRR, abs/1206.6293, 2012.
  • 22. Punnoose R., Crainiceanu A., Rapp D.: Rya: A Scalable RDF Triple Store for the Clouds. Proceedings of the 1st International Workshop on Cloud Intelligence, Cloud-I ’12, ACM, New York, NY, USA 2012.
  • 23. Ramanathan S., Goel S., Alagumalai S.: Comparison of Cloud database: Amazon’s SimpleDB and Google’s Bigtable. 2011 International Conference on Recent Trends in Information Systems (ReTIS), IEEE, 2011, p. 165÷168.
  • 24. Ravindra P., HyeongSik K., Anyanwu K.: An intermediate algebra for optimizing RDF graph pattern matching on MapReduce. In The Semantic Web: Research and Applica-tions, Springer, 2011, p. 46÷61.
  • 25. Rohloff K., Schantz R.E.: Clause-iteration with MapReduce to scalably query data-graphs in the SHARD graph-store. Proceedings of the fourth international workshop on Data-intensive distributed computing, ACM, 2011, p. 35÷44.
  • 26. Fielding R.T.: Architectural styles and the design of network-based software architectu-res. Diss. University of California, Irvine 2000.
  • 27. Schätzle A., Przyjaciel-Zablocki M., Hornung T., Lausen G.: PigSPARQL: a SPARQL query processing baseline for big data. Proceedings of the 2013th International Confer-ence on Posters & Demonstrations Track-Volume 1035, CEUR-WS. Org, 2013, p. 241÷244.
  • 28. Schätzle A., Przyjaciel-Zablocki M., Skilevic S., Lausen G.: S2RDF: RDF Querying with SPARQL on Spark. arXiv preprint arXiv:1512.07021, 2015.
  • 29. Stein R., Zacharias V.: RDF on cloud number nine. 4th Workshop on New Forms of Reasoning for the Semantic Web: Scalable and Dynamic, 2010, p. 11÷23.
  • 30. Tomaszuk D., Rybiński H.: Grouping Multiple RDF Graphs in the Collections. Interna-tional Conference: Beyond Databases, Architectures and Structures, Communications in Computer and Information Systems, Vol. 424, Springer International Publishing, 2014.
  • 31. Tomaszuk D., Skonieczny Ł., Wood D.: RDF graph partitions: A brief survey. Interna-tional Conference: Beyond Databases, Architectures and Structures, Communications in Computer and Information Systems, Vol. 521, Springer International Publishing, 2015.
  • 32. Tomaszuk D.: Named graphs in RDF/JSON serialization. Zeszyty Naukowe Politechni-ki Gdańskiej, 2011, p. 273÷278.
  • 33. Zeng K., Yang J., Wang H., Shao B., Wang Z.: A distributed graph engine for web scale RDF data. Proceedings of the VLDB Endowment, Vol. 6(4), 2013, p. 265÷276.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-85a62964-69a1-4b7e-8cfe-e1cf4fdc4ab4
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