No CrossRef data available.
Article contents
Precomputing Datalog Evaluation Plans in Large-Scale Scenarios
Published online by Cambridge University Press: 20 September 2019
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
With the more and more growing demand for semantic Web services over large databases, an efficient evaluation of Datalog queries is arousing a renewed interest among researchers and industry experts. In this scenario, to reduce memory consumption and possibly optimize execution times, the paper proposes novel techniques to determine an optimal indexing schema for the underlying database together with suitable body-orderings for the Datalog rules. The new approach is compared with the standard execution plans implemented in DLV over widely used ontological benchmarks. The results confirm that the memory usage can be significantly reduced without paying any cost in efficiency.
Information
- Type
- Original Article
- Information
- Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 1073 - 1089
- Copyright
- © Cambridge University Press 2019
References
Allocca, C., Calimeri, F., Civili, C., Costabile, R., Cuteri, B., Fiorentino, A., Fuscà, D., Germano, S., Laboccetta, G., Manna, M., Perri, S., Reale, K., Ricca, F., Veltri, P., and Zangari, J. 2019. Large-scale reasoning on expressive horn ontologies. In Proceedings of Datalog 2.0, Alviano, M. and Pieris, A., Eds. CEUR Workshop Proceedings, vol. 2368. CEUR-WS.org, 10–21.Google Scholar
Allocca, C., Costabile, R., Fiorentino, A., Perri, S., and Zangari, J. 2019. Memory-saving evaluation plans for datalog. In JELIA. Lecture Notes in Computer Science, vol. 11468. Springer, 453–461.Google Scholar
Alviano, M., Calimeri, F., Dodaro, C., Fuscà, D., Leone, N., Perri, S., Ricca, F., Veltri, P., and Zangari, J. 2017. The ASP system DLV2. In Proc. of LPNMR. LNCS, vol. 10377. 215–221.Google Scholar
Alviano, M., Faber, W., Greco, G., and Leone, N. 2012. Magic sets for disjunctive datalog programs. Artificial Intelligence 187, 156–192.Google Scholar
Amendola, G., Leone, N., and Manna, M. 2018. Finite controllability of conjunctive query answering with existential : Two steps forward. In IJCAI. 5189–5193.Google Scholar
Cal, A., Gottlob, G., and Lukasiewicz, T. 2009. Tractable query answering over ontologies with datalog+/-. In Proceedings of DL’09.Google Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F., and Schaub, T. 2012. Asp-core-2: Input language format. https://www.mat.unical.it/aspcomp2013/files/ASP-CORE-2.03b.pdf.Google Scholar
Calimeri, F., Fuscà, D., Perri, S., and Zangari, J. 2017. I-DLV: the new intelligent grounder of DLV. Intelligenza Artificiale 11, 1, 5–20.Google Scholar
Calimeri, F., Perri, S., and Zangari, J. 2019. Optimizing answer set computation via heuristic-based decomposition. TPLP, 1–26.Google Scholar
Carral, D., Dragoste, I., and Krötzsch, M. 2018. The combined approach to query answering in horn-alchoiq. In KR. AAAI Press, 339–348.Google Scholar
Eiter, T., Ortiz, M., Simkus, M., Tran, T., and Xiao, G. 2012. Query rewriting for horn-shiq plus rules. In Proc. of AAAI.Google Scholar
Gelfond, M. and Lifschitz, V. 1991a. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 3/4, 365–385.Google Scholar
Gelfond, M. and Lifschitz, V. 1991b. Classical negation in logic programs and disjunctive databases. New Generation Comput. 9, 3/4, 365–386.Google Scholar
Grau, B. C., Motik, B., Stoilos, G., and Horrocks, I. 2012. Completeness guarantees for incomplete ontology reasoners: Theory and practice. J. Artif. Intell. Res. 43, 419–476.Google Scholar
Kontchakov, R., Lutz, C., Toman, D., Wolter, F., and Zakharyaschev, M. 2011. The combined approach to ontology-based data access. In IJCAI.Google Scholar
Leone, N., Allocca, C., Alviano, M., Calimeri, F., Civili, C., Costabile, R., Fiorentino, A., Fuscà, D., Germano, S., Laboccetta, G., Cuteri, B., Manna, M., Perri, S., Reale, K., Ricca, F., Veltri, P., and Zangari, J. 2019. Enhancing DLV for large-scale reasoning. In Proceedings of LPNMR, Balduccini, M., Lierler, Y., and Woltran, S., Eds. Lecture Notes in Computer Science, vol. 11481. Springer, 312–325.Google Scholar
Lutz, C., Seylan, I., Toman, D., and Wolter, F. 2013. The combined approach to OBDA: taming role hierarchies using filters. In International Semantic Web Conference (1). Lecture Notes in Computer Science, vol. 8218. Springer, 314–330.Google Scholar
Mora, J. and Corcho, Ó. 2013. Towards a systematic benchmarking of ontology-based query rewriting systems. In International Semantic Web Conference (2). LNCS, vol. 8219. Springer, 376–391.Google Scholar
Ortiz, M. 2013. Ontology based query answering: The story so far. In AMW. CEUR Workshop Proceedings, vol. 1087. CEUR-WS.org.Google Scholar
Stefanoni, G., Motik, B., and Horrocks, I. 2012. Small datalog query rewritings for EL. In DL. CEUR Workshop Proceedings, vol. 846.Google Scholar
Xiao, G., Calvanese, D., Kontchakov, R., Lembo, D., Poggi, A., Rosati, R., and Zakharyaschev, M. 2018. Ontology-based data access: A survey. In IJCAI.Google Scholar
You have
Access