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GRaCe: A Relaxed Approach for Graph Query Caching

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12011))

Abstract

SPARQL query optimization is an important issue for RDF data stores that can benefit from the usage of caching frameworks. Most caching approaches rely on a precise match semantics, that limits the number of cache hits and, as a consequence, the potential benefit. Others propose relaxed matches for the entire query, which is precisely executed over the cached result set. In this paper, to overcome these limitations we propose GRaCe, a Graph Relaxed Caching approach for RDF data stores. GRaCe supports relaxed cache matches and a relaxed query semantics, thus increasing the number of cache hits. Experimental results show that a relaxed cache can significantly reduce query execution time in all the scenarios where a relaxed query result is tolerated.

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Notes

  1. 1.

    https://jena.apache.org/documentation/tdb/.

References

  1. Chekol, M.W., Euzenat, J., Genevès, P., Layaïda, N.: SPARQL query containment under RDFS entailment regime. In: Gramlich, B., Miller, D., Sattler, U. (eds.) IJCAR 2012. LNCS (LNAI), vol. 7364, pp. 134–148. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31365-3_13

    Chapter  Google Scholar 

  2. De Fino, F.: Relaxation meets caching: towards smart caching approaches for graph query processing. Ph.D. thesis. University of Genova, Italy (2020, in preparation)

    Google Scholar 

  3. Fard, A., et al.: Effective caching techniques for accelerating pattern matching queries. In: Big Data 2014, pp. 491–499 (2014)

    Google Scholar 

  4. Frosini, R., et al.: Flexible query processing for SPARQL. Semant. Web 8(4), 533–563 (2017)

    Article  Google Scholar 

  5. Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. J. Web Semant. 3(2–3), 158–182 (2005)

    Article  Google Scholar 

  6. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  7. Junttila, T., Kaski, P.: Engineering an efficient canonical labeling tool for large and sparse graphs. In: International Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 135–149 (2007)

    Chapter  Google Scholar 

  8. Lorey, J., Naumann, F.: Caching and prefetching strategies for SPARQL queries. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 46–65. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_5

    Chapter  Google Scholar 

  9. Mailis, T., et al.: An efficient index for RDF query containment. In: SIGMOD Conference 2019, pp. 1499–1516 (2019)

    Google Scholar 

  10. Martin, M., Unbehauen, J., Auer, S.: Improving the performance of semantic web applications with SPARQL query caching. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6089, pp. 304–318. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13489-0_21

    Chapter  Google Scholar 

  11. Papailiou, N., et al.: Graph-aware, workload-adaptive SPARQL query caching. In: SIGMOD Conference 2015, pp. 1777–1792 (2015)

    Google Scholar 

  12. Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF, W3C recommendation (2008). https://www.w3.org/TR/rdf-sparql-query/

  13. Stocker, M., et al.: SPARQL basic graph pattern optimization using selectivity estimation. In: WWW 2008, pp. 595–604 (2008)

    Google Scholar 

  14. Wang, J., et al.: GC: a graph caching system for subgraph/supergraph queries. PVLDB 11(12), 2022–2025 (2018)

    Google Scholar 

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Correspondence to Francesco De Fino .

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De Fino, F., Catania, B., Guerrini, G. (2020). GRaCe: A Relaxed Approach for Graph Query Caching. In: Chatzigeorgiou, A., et al. SOFSEM 2020: Theory and Practice of Computer Science. SOFSEM 2020. Lecture Notes in Computer Science(), vol 12011. Springer, Cham. https://doi.org/10.1007/978-3-030-38919-2_55

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  • DOI: https://doi.org/10.1007/978-3-030-38919-2_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38918-5

  • Online ISBN: 978-3-030-38919-2

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