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FLUX: from SQL to GQL query translation tool

Published:27 January 2021Publication History

ABSTRACT

With the influx of Web 3.0 the focus in Big Data Analytics has shifted towards modelling highly interconnected data and analysing relationships between them. Graph databases befit the requirements of Big Data Analytics yet organizations still depend on relational databases. A major roadblock in the industry wide adoption of graph databases is that a standard query language is still in its inception stage hence withholding interoperability between the two technologies. In this research we propose a tool FLUX for translating relational database queries to graph database queries.

References

  1. Averbuch Alex and Martinez Norbert. 2013. LDBC Use case analysis and choke point analysis. http://ldbcouncil.org/sites/default/files/LDBC_D3.3.1.pdf Accessed: 2019-03-01.Google ScholarGoogle Scholar
  2. Renzo Angles, Marcelo Arenas, Pablo Barceló, Peter Boncz, George Fletcher, Claudio Gutierrez, Tobias Lindaaker, Marcus Paradies, Stefan Plantikow, Juan Sequeda, et al. 2018. G-CORE: A core for future graph query languages. In Proceedings of the 2018 International Conference on Management of Data. 1421--1432.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reutter, and Domagoj Vrgoč. 2017. Foundations of modern query languages for graph databases. ACM Computing Surveys (CSUR) 50, 5 (2017), 1--40.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Renzo Angles, Peter Boncz, Josep Larriba-Pey, Irini Fundulaki, Thomas Neumann, Orri Erling, Peter Neubauer, Norbert Martinez-Bazan, Venelin Kotsev, and Ioan Toma. 2014. The linked data benchmark council: a graph and RDF industry benchmarking effort. ACM SIGMOD Record 43, 1 (2014), 27--31.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Thiago Henrique Dias Araujo, Barbara Tieko Agena, Kelly Rosa Braghetto, and Renata Wassermann. 2017. OntoMongo-Ontology-Based Data Access for NoSQL. ONTOBRAS 1908 (2017), 55--66.Google ScholarGoogle Scholar
  6. Nikos Bikakis, Nektarios Gioldasis, Chrisa Tsinaraki, and Stavros Christodoulakis. 2009. Querying xml data with sparql. In International conference on database and expert systems applications. Springer, 372--381.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Peter Boncz, Irini Fundulaki, Andrey Gubichev, Josep Larriba-Pey, and Thomas Neumann. 2013. The linked data benchmark council project. Datenbank-Spektrum 13, 2 (2013), 121--129.Google ScholarGoogle ScholarCross RefCross Ref
  8. Diego Calvanese, Giuseppe De Giacomo, Maurizio Lenzerini, and Moshe Y Vardi. 2000. Answering regular path queries using views. In Proceedings of 16th International Conference on Data Engineering (Cat. No. 00CB37073). IEEE, 389--398.Google ScholarGoogle ScholarCross RefCross Ref
  9. Diego Calvanese, Giuseppe De Giacomo, Maurizio Lenzerini, and Moshe Y Vardi. 2000. Containment of conjunctive regular path queries with inverse. KR 2000 (2000), 176--185.Google ScholarGoogle Scholar
  10. Stefano Ceri and Georg Gottlob. 1985. Translating SQL into relational algebra: Optimization, semantics, and equivalence of SQL queries. IEEE Transactions on software engineering 4 (1985), 324--345.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Donald D Chamberlin and Raymond F Boyce. 1974. SEQUEL: A structured English query language. In Proceedings of the 1974 ACM SIGFIDET (now SIGMOD) workshop on Data description, access and control. 249--264.Google ScholarGoogle Scholar
  12. Sandeep Chanda and Damien Foggon. 2013. The Future of Relational Databases. In Beginning ASP. NET 4.5 Databases. Springer, 27--47.Google ScholarGoogle Scholar
  13. Artem Chebotko, Shiyong Lu, Hasan M Jamil, and Farshad Fotouhi. 2006. Semantics preserving SPARQL-to-SQL query translation for optional graph patterns. Wayne State University, Tech. Rep. TR-DB-052006-CLJF (2006).Google ScholarGoogle Scholar
  14. Edgar F Codd. 2002. A relational model of data for large shared data banks. In Software pioneers. Springer, 263--294.Google ScholarGoogle Scholar
  15. David Dominguez-Sal, Norbert Martinez-Bazan, Victor Muntes-Mulero, Pere Baleta, and Josep Lluis Larriba-Pey. 2010. A discussion on the design of graph database benchmarks. In Technology Conference on Performance Evaluation and Benchmarking. Springer, 25--40.Google ScholarGoogle Scholar
  16. Matthias Droop, Markus Flarer, Jinghua Groppe, Sven Groppe, Volker Linnemann, Jakob Pinggera, Florian Santner, Michael Schier, Felix Schöpf, Hannes Staffler, et al. 2007. Translating xpath queries into sparql queries. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, 9--10.Google ScholarGoogle ScholarCross RefCross Ref
  17. Wenfei Fan, Jeffrey Xu Yu, Hongjun Lu, Jianhua Lu, and Rajeev Rastogi. 2005. Query translation from XPath to SQL in the presence of recursive DTDs. In VLDB. 337--348.Google ScholarGoogle Scholar
  18. Peter Gulutzan and Trudy Pelzer. 1999. SQL-99 complete, really. CMP books.Google ScholarGoogle Scholar
  19. Alan Halverson, Vanja Josifovski, Guy Lohman, Hamid Pirahesh, and Mathias Mörschel. 2004. ROX: relational over XML. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 264--275.Google ScholarGoogle Scholar
  20. Jelle Hellings. 2018. On Tarski's Relation Algebra: querying trees and chains and the semi-join algebra.Google ScholarGoogle Scholar
  21. Jelle Hellings, Yuqing Wu, Marc Gyssens, and Dirk Van Gucht. 2018. The power of Tarski's relation algebra on trees. In International Symposium on Foundations of Information and Knowledge Systems. Springer, 244--264.Google ScholarGoogle ScholarCross RefCross Ref
  22. Moath Jarrah, Bahaa Al-khatieb, Naseem Mahasneh, Baghdad Al-khateeb, and Yaser Jararweh. 2020. GDBApex: A graph-based system to enable efficient transformation of enterprise infrastructures. Software: Practice and Experience (2020).Google ScholarGoogle Scholar
  23. Ross D King, Jem Rowland, Stephen G Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa N Soldatova, et al. 2009. The automation of science. Science 324, 5923 (2009), 85--89.Google ScholarGoogle Scholar
  24. Mohamed Nadjib Mami, Damien Graux, Harsh Thakkar, Simon Scerri, Sören Auer, and Jens Lehmann. 2019. The query translation landscape: a survey. arXiv preprint arXiv:1910.03118 (2019).Google ScholarGoogle Scholar
  25. Erik Meijer and Gavin Bierman. 2011. A co-relational model of data for large shared data banks. Commun. ACM 54, 4 (2011), 49--58.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. OpenCypher. 2018. OpenCypher. https://www.opencypher.org/ Accessed: 2018-10-01.Google ScholarGoogle Scholar
  27. Jaroslav Pokornỳ. 2015. Graph Databases: Their Power and Limitations. In 14th Computer Information Systems and Industrial Management (CISIM). Springer, 58--69.Google ScholarGoogle Scholar
  28. Jyothsna Rachapalli, Vaibhav Khadilkar, Murat Kantarcioglu, and Bhavani Thuraisingham. 2011. RETRO: a framework for semantics preserving SQL-to-SPARQL translation. The University of Texas at Dallas 800 (2011), 75080--3021.Google ScholarGoogle Scholar
  29. Marko A Rodriguez and Peter Neubauer. 2010. Constructions from dots and lines. Bulletin of the American Society for Information Science and Technology 36, 6 (2010), 35--41.Google ScholarGoogle ScholarCross RefCross Ref
  30. Marko A Rodriguez and Peter Neubauer. 2012. The graph traversal pattern. In Graph Data Management: Techniques and Applications. IGI Global, 29--46.Google ScholarGoogle Scholar
  31. Chandan Sharma and Roopak Sinha. 2019. A Schema-First Formalism for Labeled Property Graph Databases: Enabling Structured Data Loading and Analytics. In Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. 71--80.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Chandan Sharma, Roopak Sinha, and Paulo Leitao. 2019. IASelect: Finding Bestfit Agent Practices in Industrial CPS Using Graph Databases. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Vol. 1. IEEE, 1558--1563.Google ScholarGoogle Scholar
  33. Avi Silberschatz, Michael Stonebraker, and Jeff Ullman. 1991. Database systems: Achievements and opportunities. Commun. ACM 34, 10 (1991), 110--120.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Benjamin A Steer, Alhamza Alnaimi, Marco ABFG Lotz, Felix Cuadrado, Luis M Vaquero, and Joan Varvenne. 2017. Cytosm: Declarative property graph queries without data migration. In Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems. 1--6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Wen Sun, Achille Fokoue, Kavitha Srinivas, Anastasios Kementsietsidis, Gang Hu, and Guotong Xie. 2015. Sqlgraph: An efficient relational-based property graph store. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 1887--1901.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Igor V Tetko, Ola Engkvist, Uwe Koch, Jean-Louis Reymond, and Hongming Chen. 2016. BIGCHEM: Challenges and opportunities for big data analysis in chemistry. Molecular informatics 35, 11--12 (2016), 615--621.Google ScholarGoogle Scholar
  37. Harsh Thakkar, Dharmen Punjani, Yashwant Keswani, Jens Lehmann, and Sören Auer. 2018. A Stitch in Time Saves Nine-SPARQL querying of Property Graphs using Gremlin Traversals. arXiv preprint arXiv:1801.02911 (2018).Google ScholarGoogle Scholar
  38. JV Tucker and K Stephenson. 2003. Data, syntax and semantics.Google ScholarGoogle Scholar
  39. Ted Wilmes. 2016. Gremlin to SQL. https://github.com/twilmes/sql-gremlin Accessed: 2020--03-01.Google ScholarGoogle Scholar
  40. Chun Zhang, Jeffrey Naughton, David DeWitt, Qiong Luo, and Guy Lohman. 2001. On supporting containment queries in relational database management systems. In Proceedings of the 2001 ACM SIGMOD international conference on Management of data. 425--436.Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering
      December 2020
      1449 pages
      ISBN:9781450367684
      DOI:10.1145/3324884

      Copyright © 2020 ACM

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      Publication History

      • Published: 27 January 2021

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