Abstract
According to the predictive results of query performance, queries can be rewritten to reduce time cost or rescheduled to the time when the resource is not in contention. As more large RDF datasets appear on the Web recently, predicting performance of SPARQL query processing is one major challenge in managing a large RDF dataset efficiently. In this paper, we focus on representing SPARQL queries with feature vectors and using these feature vectors to train predictive models that are used to predict the performance of SPARQL queries. The evaluations performed on real world SPARQL queries demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches.
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Graph edit distance is the minimum amount of edit operations (i.e., deletion, insertion and substitutions of nodes and edges) needed to transform one graph to the other.
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References
Ahmad, M., Duan, S., Aboulnaga, A., Babu, S.: Predicting completion times of batch query workloads using interaction-aware models and simulation. In: Proceedings of the 14th International Conference on Extending Database Technology (EDBT 2011), Uppsala, pp. 449–460, March 2011
Akdere, M., Çetintemel, U., Riondato, M., Upfal, E., Zdonik, S.B.: Learning-based query performance modeling and prediction. In: Proceedings of the 28th International Conference on Data Engineering (ICDE 2012), Washington, DC, pp. 390–401, April 2012
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Bursztyn, D., Goasdoué, F., Manolescu, I.: Optimizing reformulation-based query answering in RDF. In: Proceedings of the 18th International Conference on Extending Database Technology (EDBT 2015), Brussels, pp. 265–276, March 2015
Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)
Ganapathi, A., Kuno, H.A., Dayal, U., Wiener, J.L., Fox, A., Jordan, M.I., Patterson, D.A.: Predicting multiple metrics for queries: better decisions enabled by machine learning. In: Proceedings of the 25th International Conference on Data Engineering (ICDE 2009), Shanghai, pp. 592–603, March 2009
Gubichev, A., Neumann, T.: Exploiting the query structure for efficient join ordering in SPARQL queries. In: Proceedings of the 17th International Conference on Extending Database Technology (EDBT 2014), Athens, pp. 439–450, March 2014
Hasan, R.: Predicting SPARQL query performance and explaining linked data. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 795–805. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07443-6_53
Li, J., König, A.C., Narasayya, V.R., Chaudhuri, S.: Robust estimation of resource consumption for SQL queries using statistical techniques. VLDB Endow. (PVLDB) 5(11), 1555–1566 (2012)
Morsey, M., Lehmann, J., Auer, S., Ngomo, A.N.: Usage-centric benchmarking of RDF triple stores. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, July 2012
Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16 (2009)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)
Smola, A., Vapnik, V.: Support vector regression machines. Adv. Neural Inf. Process. Syst. 9, 155–161 (1997)
Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL basic graph pattern optimization using selectivity estimation. In: Proceedings of the 17th International World Wide Web Conference (WWW 2008), Beijing, pp. 595–604, April 2008
Tsialiamanis, P., Sidirourgos, L., Fundulaki, I., Christophides, V., Boncz, P. A.: Heuristics-based query optimisation for SPARQL. In: Proceedings of the 15th International Conference on Extending Database Technology (EDBT 2012), Uppsala, pp. 324–335, March 2012
Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigümüs, H., Naughton, J.F.: Predicting query execution time: are optimizer cost models really unusable? In: Proceedings of the 29th International Conference on Data Engineering (ICDE 2013), Brisbane, pp. 1081–1092, April 2013
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A.F.M., Liu, B., Yu, P.S., Zhou, Z., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
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Zhang, W.E., Sheng, Q.Z., Taylor, K., Qin, Y., Yao, L. (2016). Learning-Based SPARQL Query Performance Prediction. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_23
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DOI: https://doi.org/10.1007/978-3-319-48740-3_23
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