Distributed Efficient Provenance-Aware Regular Path Queries on Large RDF Graphs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

With the proliferation of knowledge graphs, massive RDF graphs have been published on the Web. As an essential type of queries for RDF graphs, Regular Path Queries (RPQs) have been attracting increasing research efforts. However, the existing query processing approaches mainly focus on the standard semantics of RPQs, which cannot provide provenance of the answer sets. We propose dProvRPQ that is a distributed approach to evaluating provenance-aware RPQs over big RDF graphs. Our Pregel-based method employs Glushkov automata to keep track of matching processes of RPQs in parallel. Meanwhile, four optimization strategies are devised, including edge filtering, candidate states, message compression, and message selection, which can reduce the intermediate results of the basic dProvRPQ algorithm dramatically and overcome the counting-paths problem to some extent. The proposed algorithms are verified by extensive experiments on both synthetic and real-world datasets, which show that our approach can efficiently answer the provenance-aware RPQs over large RDF graphs.

Keywords

Regular path query Provenance-aware RDF graph Pregel 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61572353, 61772361), the National High-tech R&D Program of China (863 Program) (2013AA013204), and the Natural Science Foundation of Tianjin (17JCYBJC15400).

References

  1. 1.
    Arenas, M., Conca, S., Pérez, J.: Counting beyond a Yottabyte, or how SPARQL 1.1 property paths will prevent adoption of the standard. In: Proceedings of the 21st International Conference on World Wide Web, pp. 629–638. ACM (2012)Google Scholar
  2. 2.
    Barceló, P., Libkin, L., Lin, A.W., Wood, P.T.: Expressive languages for path queries over graph-structured data. ACM Trans. Database Syst. (TODS) 37(4), 31 (2012)CrossRefGoogle Scholar
  3. 3.
    Brüggemann-Klein, A.: Regular expressions into finite automata. Theoret. Comput. Sci. 120(2), 197–213 (1993)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Calvanese, D., De Giacomo, G., Lenzerini, M., Vardi, M.Y.: Answering regular path queries using views. In: 16th International Conference on Data Engineering, Proceedings, pp. 389–398. IEEE (2000)Google Scholar
  5. 5.
    Dey, S., Cuevas-Vicenttín, V., Köhler, S., Gribkoff, E., Wang, M., Ludäscher, B.: On implementing provenance-aware regular path queries with relational query engines. In: Proceedings of the Joint EDBT/ICDT 2013 Workshops, pp. 214–223. ACM (2013)Google Scholar
  6. 6.
    Harris, S., Seaborne, A., Prudhommeaux, E.: SPARQL 1.1 query language. W3C Recomm. 21(10) (2013). https://www.w3.org/TR/sparql11-query/
  7. 7.
    Jupp, S., Malone, J., Bolleman, J., Brandizi, M., Davies, M., Garcia, L., Gaulton, A., Gehant, S., Laibe, C., Redaschi, N., et al.: The EBI RDF platform: linked open data for the life sciences. Bioinformatics 30(9), 1338–1339 (2014)CrossRefGoogle Scholar
  8. 8.
    Koschmieder, A., Leser, U.: Regular path queries on large graphs. In: Ailamaki, A., Bowers, S. (eds.) SSDBM 2012. LNCS, vol. 7338, pp. 177–194. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31235-9_12CrossRefGoogle Scholar
  9. 9.
    Kostylev, E.V., Reutter, J.L., Romero, M., Vrgoč, D.: SPARQL with property paths. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 3–18. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25007-6_1CrossRefGoogle Scholar
  10. 10.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 135–146. ACM (2010)Google Scholar
  11. 11.
    Nolé, M., Sartiani, C.: Regular path queries on massive graphs. In: Proceedings of the 28th International Conference on Scientific and Statistical Database Management, p. 13. ACM (2016)Google Scholar
  12. 12.
    Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web 19(6), 1151–1177 (2016)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Ling, J., Wang, J., Wang, K., Feng, Z.: Answering provenance-aware regular path queries on RDF graphs using an automata-based algorithm. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 395–396. ACM (2014)Google Scholar
  14. 14.
    Wang, X., Wang, J.: ProvRPQ: an interactive tool for provenance-aware regular path queries on RDF graphs. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 480–484. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46922-5_44CrossRefGoogle Scholar
  15. 15.
    Wang, X., Wang, J., Zhang, X.: Efficient distributed regular path queries on RDF graphs using partial evaluation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1933–1936. ACM (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina

Personalised recommendations