Skip to main content
SpringerLink
Log in

A Comprehensive Study for Essentiality of Graph Based Distributed SPARQL Query Processing

  • Conference paper
  • First Online:
Book cover Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10829))

Included in the following conference series:

Abstract

Distributed SPARQL query processing frameworks are categorized on the bases of query computation into relation, graph and hybrid based distributed query computing. By exploring the historical achievements under these umbrellas we try to motivate the researchers, to define such a framework for Graph Based Distributed SPARQL Query Processing, which supports Full of SPARQL and also explains the principles for employing optimization. In this study we elaborate all popular existing frameworks for distributed query processing and organize a comparative study according to the facts and figures. We identify different limitations and discrepancies in all approaches e.g. only few support the Full of SPARQL, all these are optimized for different kind of benchmarks and all carries own partitioning strategy. We study some valuable query optimization techniques and their implementation. How these techniques are employed in distributed environment. Finally, some future work is highlighted on Graph Based Distributed SPARQL Query Processing which will support all features of SPARQL 1.1 and well optimized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. W3C: RDF Primer. http://www.w3.org/TR/rdf-primer/. Accessed 1 Mar 2018

  2. W3C: RDF 1.1. https://www.w3.org/TR/rdf11-new/. Accessed 4 Mar 2018

  3. Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (2008)

    Google Scholar 

  4. DBpedia. http://dbpedia.org/. Accessed 3 Mar 2018

  5. PubChemRDF. http://pubchem.ncbi.nlm.nih.gov/rdf/. Accessed 26 Feb 2018

  6. Bio2RDF. http://bio2rdf.org/. Accessed 20 Feb 2018

  7. UniProt. http://www.uniprot.org/. Accessed 21 Feb 2018

  8. SPARQL1.1. https://www.w3.org/TR/sparql11-query/. Accessed 4 Mar 2018

  9. Koutris, P.: Query processing for massively parallel systems, University of Washington, pp. 2–5 (2015)

    Google Scholar 

  10. Zeng, K., Yang, J., Wang, H., Shao, B., Wang, Z.: A distributed graph engine for web scale RDF data. Proc. VLDB Endow. 6, 265–276 (2013)

    Article  Google Scholar 

  11. Schätzle, A., Przyjaciel-Zablocki, M., Lausen, G.: PigSPARQL: mapping SPARQL to Pig Latin. In: Proceedings of SWIM 2011, pp. 4:1–4:8 (2011)

    Google Scholar 

  12. Hose, K., Schenkel, R.: WARP: workload-aware replication and partitioning for RDF. In: Proceedings of ICDE 2013 Workshops (2013)

    Google Scholar 

  13. Gurajada, S., Seufert, S., Miliaraki, I., Theobald, M.: TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing. In: Proceedings of SIGMOD (2014)

    Google Scholar 

  14. Schätzle, A., Przyjaciel-Zablocki, M., Neu, A., Lausen, G.: Sempala: interactive SPARQL query processing on hadoop. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 164–179. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11964-9_11

    Chapter  Google Scholar 

  15. Kaoudi, Z., Manolescu, I., Zampetakis, S.: CliqueSquare: flat plans for massively parallel RDF queries. In: Proceedings of ICDE 2015, pp. 771–782 (2015)

    Google Scholar 

  16. Hammoud, M., Rabbou, D.A., Nouri, R., Beheshti, S.-M.-R., Sakr, S.: DREAM: distributed RDF engine with adaptive query planner and minimal communication. Proc. VLDB 8(6), 654–665 (2015)

    Article  Google Scholar 

  17. Schätzle, A., Przyjaciel-Zablocki, M., Berberich, T., Lausen, G.: S2X: graph-parallel querying of RDF with GraphX. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds.) Big-O(Q)/DMAH -2015. LNCS, vol. 9579, pp. 155–168. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41576-5_12

    Chapter  Google Scholar 

  18. Harbi, R., Abdelaziz, I., Kalnis, P., Mamoulis, N., Ebrahim, Y., Sahli, M.: Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning. VLDB J. 25(3), 355–380 (2016)

    Article  Google Scholar 

  19. Peng, P., Zou, L., Özsu, M.T., Chen, L., Zhao, D.: Processing SPARQL queries over distributed RDF graphs. VLDB J. 25(2), 243–268 (2016)

    Article  Google Scholar 

  20. Schätzle, A., Przyjaciel-Zablocki, M., Skilevic, S., Lausen, G.: S2RDF: RDF querying with SPARQL on Spark. Proc. VLDB 9(10), 804–815 (2016)

    Article  Google Scholar 

  21. Dadhaniya, D.R., Makwana, A.: Survey paper for different SPARQL query optimization techniques. MJSRE J. 2(8), 83–85 (2016)

    Google Scholar 

  22. Özsu, M.T.: A survey of RDF data management systems. Front. Comput. Sci. 10(3), 418–432 (2016)

    Article  Google Scholar 

  23. Ma, Z., Capretz, M.A.M., Yan, L.: Storing massive resource description framework (RDF) data: a survey. Knowl. Eng. Rev 31(4), 391–413 (2016)

    Article  Google Scholar 

  24. Abdelaziz, I., Harbi, R., Khayyat, Z., Kalnis, P.: A survey and experimental comparison of distributed SPARQL engines for very large RDF data. Proc. VLDB 10(13), 2049–2060 (2017)

    Article  Google Scholar 

  25. Aljanaby, A., Abuelrub, E., Odeh, M.: A survey of distributed query optimization. Int. Arab J. Inf. Technol. 2(1), 48–57 (2005)

    Google Scholar 

  26. Wilkinson, K., Sayers, C., Kuno, H., Reynolds, D.: Efficient RDF storage and retrieval in Jena2. In: Proceedings of SWDB, pp. 131–150 (2003)

    Google Scholar 

  27. Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: Scalable semantic Web data management using vertical partitioning. In: Proceedings of VLDB 2007, pp. 411–423. (2007)

    Google Scholar 

  28. Schätzle, A.: Distributed RDF querying on hadoop, University of Freiburg, pp. 124–127 (2016)

    Google Scholar 

  29. Gonzalez, J.E., Xin, R.S., Dave, A., Crankshaw, D., Franklin, M.J., Stoica, I.: GraphX: graph processing in a distributed dataflow framework. In: Proceedings of 11th USENIX OSDI 2014, pp. 599–613 (2014)

    Google Scholar 

  30. Özsu, M.T., Valduriez, P.: Optimization of distributed queries. In: Özsu, M.T., Valduriez, P. (eds.) Principles of Distributed Database Systems, 3rd edn, pp. 245–295. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-8834-8_8

    Chapter  Google Scholar 

  31. Hartig, O., Heese, R.: The SPARQL query graph model for query optimization. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 564–578. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72667-8_40

    Chapter  Google Scholar 

  32. Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C.: SPARQL basic graph pattern optimization using selectivity estimation. In: Proceedings of WWW 2008, pp. 595–604 (2008)

    Google Scholar 

  33. Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB 1(1), 647–659 (2008)

    Article  Google Scholar 

  34. Huang, H., Liu, C.: Estimating selectivity for joined RDF triple patterns. In: Proceedings of CIKM 2011, pp. 1435–1444 (2011)

    Google Scholar 

  35. Ladwig, G., Tran, T.: Linked data query processing strategies. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 453–469. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17746-0_29

    Chapter  Google Scholar 

  36. Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K.-U., Umbrich, J.: Data summaries for on-demand queries over linked data. In: Proceedings of 19th WWW 2010 (2010)

    Google Scholar 

  37. Hartig, O., Bizer, C., Freytag, J.-C.: Executing SPARQL queries over the web of linked data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 293–309. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_19

    Chapter  Google Scholar 

  38. Wang, X., Tiropanis, T., Davis, H.C.: Evaluating graph traversal algorithms for distributed SPARQL query optimization. In: Pan, J.Z., Chen, H., Kim, H.-G., Li, J., Wu, Z., Horrocks, I., Mizoguchi, R., Wu, Z. (eds.) JIST 2011. LNCS, vol. 7185, pp. 210–225. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29923-0_14

    Chapter  Google Scholar 

  39. Vandervalk, B.P., McCarthy, E.L., Wilkinson, M.D.: Optimization of distributed SPARQL queries using Edmonds algorithm and Prims algorithm. In: Proceedings of CSE 2009, pp. 330–337 (2009)

    Google Scholar 

  40. Prim, R.C.: Shortest connection networks and some generalizations. Bell Syst. Tech. J. 36(6), 1389–1401 (1957)

    Article  Google Scholar 

  41. Edmonds, J.: Optimum branchings. J. Res. Natl. Bur. Stand. 71B, 233–240 (1967)

    Article  MathSciNet  Google Scholar 

  42. Reddy, B.R.K., Kumar, P.S.: Optimizing SPARQL queries over the web of linked data. In: Proceedings Workshop on Semantic Data Management (VLDB) (2010)

    Google Scholar 

  43. Atre, M., Chaoji, V., Zaki, M.J., Hendler, J.A.: Matrix bit loaded: a scalable lightweight join query Processor for RDF data. In: Proceedings of WWW 2010, pp. 41–50 (2010)

    Google Scholar 

  44. Neumann, T., Weikum, G.: The RDF-3X engine for scalable management of RDF data. VLDB J. 19(1), 91–113 (2010)

    Article  Google Scholar 

  45. Polleres, A., Peter, J.: On the relation between SPARQL 1.1 and answer set programming. J. Appl. Non-Class. Logics 23(1–2), 159–212 (2013)

    Article  MathSciNet  Google Scholar 

  46. Angles, R., Gutierrez, C.: The expressive power of SPARQL. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 114–129. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88564-1_8

    Chapter  Google Scholar 

  47. Kostylev, E.V., Reutter, J.L., Romero, M., Vrgoč, D.: SPARQL with property paths. In: Corcho, O., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 3–18. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_1

    Chapter  Google Scholar 

  48. Zhang, X.: On the primitivity of SPARQL 1.1 operators. In: Proceedings of WWW 2017, pp. 875–876 (2017)

    Google Scholar 

  49. Kontchakov, R., Kostylev, E.V: On expressibility of non-monotone operators in SPARQL. In: Proceedings of KR 2016, pp. 369–378 (2016)

    Google Scholar 

  50. Feng, J., Meng, C., Song, J., Zhang, X., Feng, Z., Zou, L.: SPARQL query parallel Processing: a survey. In: Proceedings of BigData Congress 2017, pp. 444–451 (2017)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61502336, 61672377), the National Key Research and Development Program of China (2016YFB1000603), and the Key Technology Research and Development Program of Tianjin (16YFZCGX00210).

Author information

Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Muhammad Qasim Yasin, Rafiul Haq, Zhiyong Feng & Sofonias Yitagesu

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Xiaowang Zhang

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Muhammad Qasim Yasin, Xiaowang Zhang, Rafiul Haq, Zhiyong Feng & Sofonias Yitagesu

Authors
  1. Muhammad Qasim Yasin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaowang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rafiul Haq
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sofonias Yitagesu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaowang Zhang .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Hawthorn, VIC, Australia

    Chengfei Liu

  2. Peking University, Beijing, China

    Lei Zou

  3. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yasin, M.Q., Zhang, X., Haq, R., Feng, Z., Yitagesu, S. (2018). A Comprehensive Study for Essentiality of Graph Based Distributed SPARQL Query Processing. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91455-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91454-1

  • Online ISBN: 978-3-319-91455-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation