Hostname: page-component-5b777bbd6c-7mr9c Total loading time: 0 Render date: 2025-06-24T06:27:40.555Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimenting with recursive queries in database and logic programming systems

Published online by Cambridge University Press:  01 March 2008

G. TERRACINA ,
N. LEONE ,
V. LIO  and
C. PANETTA
G. TERRACINA
Affiliation:
Dipartimento di Matematica, Università della Calabria, Via P. Bucci, 87030, Rende (CS), Italy (e-mail: terracina@mat.unical.it, leone@mat.unical.it, lio@mat.unical.it, panetta@mat.unical.it)
N. LEONE
Affiliation:
Dipartimento di Matematica, Università della Calabria, Via P. Bucci, 87030, Rende (CS), Italy (e-mail: terracina@mat.unical.it, leone@mat.unical.it, lio@mat.unical.it, panetta@mat.unical.it)
V. LIO
Affiliation:
Dipartimento di Matematica, Università della Calabria, Via P. Bucci, 87030, Rende (CS), Italy (e-mail: terracina@mat.unical.it, leone@mat.unical.it, lio@mat.unical.it, panetta@mat.unical.it)
C. PANETTA
Affiliation:
Dipartimento di Matematica, Università della Calabria, Via P. Bucci, 87030, Rende (CS), Italy (e-mail: terracina@mat.unical.it, leone@mat.unical.it, lio@mat.unical.it, panetta@mat.unical.it)
Get access Rights & Permissions [Opens in a new window]

Abstract

This article considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: (i) the quantity of data that can be handled contemporarily is limited, because reasoning is generally carried out in main-memory; (ii) the interaction with external (and independent) Database Management Systems is not trivial and, in several cases, not allowed at all; and (iii) the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This article provides a contribution in this setting; it presents a new system, called DLVDB, which aims to solve these problems. Moreover, it reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are LDL++, XSB, Smodels, and three top-level commercial Database Management Systems. DLVDB significantly outperforms even the commercial database systems on recursive queries.

Keywords

Deductive Database SystemsAnswer Set Programming/Declarative Logic Programmingrecursive queriesbenchmarks
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 8 , Issue 2 , March 2008 , pp. 129 - 165
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

American National Standards Institute. 1999. ANSI/ISO/IEC 9075-1999 (SQL:1999, Parts 1–5). American National Standards Institute, New York.Google Scholar
Abiteboul, S., Abrams, Z., Haar, S. & Milo, T. 2005. Diagnosis of asynchronous discrete event systems: Datalog to the rescue! In Proceedings of the Twenty-fourth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS, 2005), Baltimore, MD, ACM, New York, NY, USA. 358–367.CrossRefGoogle Scholar
Apt, K. R., Blair, H. A. & Walker, A. 1988. Towards a Theory of Declarative Knowledge. In Foundations of Deductive Databases and Logic Programming, Minker, J., Ed. Morgan Kaufmann, Washington, DC, 89–148.CrossRefGoogle Scholar
Arni, F., Ong, K., Tsur, S., Wang, H. & Zaniolo, C. 2003. The Deductive Database System LDL++. Journal of the Theory and Practice of Logic Programming 3, 1, 61–94.CrossRefGoogle Scholar
Balbin, I. & Ramamohanarao, K. 1987. A generalization of the differential approach to recursive query evaluation. Journal of Logic Programing 4, 3, 259–262.CrossRefGoogle Scholar
Bancilhon, F., Maier, F., Sagiv, Y. & Ullman, J. 1986. Magic sets and other strange ways to implement logic programs. In Proc. of the ACM Symposium on Principles of Database Systems (PODS'86). ACM Press, Cambridge, MA, 1–16.Google Scholar
Bancilhon, F. & Ramakrishnan, R. 1988. Performance evaluation of data intensive logic programs. In Foundations of Deductive Databases and Logic Programming. Morgan Kaufmann, Washington, DC, 439–517.CrossRefGoogle Scholar
Baral, C. 2002. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, New York.Google Scholar
Beeri, C. & Ramakrisnhan, R. 1991. On the power of magic. Journal of Logic Programming 10, 1–4, 255–259.CrossRefGoogle Scholar
Ben-Eliyahu, R. & Dechter, R. 1994. Propositional semantics for disjunctive logic programs. Annals of Mathematics and Artificial Intelligence 12, 53–87.CrossRefGoogle Scholar
Ben-Eliyahu, R. & Dechter, R. 1996. On computing minimal models. Annals of Mathematics and Artificial Intelligence 18, 1, 3–27.CrossRefGoogle Scholar
Ceri, S., Gottlob, G. & Tanca, L. 1990. Logic Programming and Databases. Springer Verlag, New York.CrossRefGoogle Scholar
Dell'Armi, T., Faber, W., Ielpa, G., Leone, N. & Pfeifer, G. 2003a. Aggregate functions in disjunctive logic programming: Semantics, complexity, and implementation in DLV. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI) 2003, Acapulco, Mexico. Morgan Kaufmann, Washington, DC, 847–852.Google Scholar
Dell'Armi, T., Faber, W., Ielpa, G., Leone, N. & Pfeifer, G. 2003b. Aggregate Functions in DLV. In Proceedings ASP03—Answer Set Programming: Advances in Theory and Implementation, Messina, Italy, de Vos, M. & Provetti, A., Eds. 274–288. URL: http://CEUR-WS.org/Vol-78/ Access date: 30/10/2006.Google Scholar
Faber, W., Leone, N., Mateis, C. & Pfeifer, G. 1999a. Using database optimization techniques for nonmonotonic reasoning. In Proceedings of the 7th International Workshop on Deductive Databases and Logic Programming (DDLP'99), I. O. Committee, Ed. Prolog Association of Japan, 135–139.Google Scholar
Faber, W., Leone, N. & Pfeifer, G. 1999b. Pushing goal derivation in DLP computations. In Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'99), El Paso, TX, Gelfond, M., Leone, N, & Pfeifer, G., Eds. Number 1730 in Lecture Notes in AI (LNAI). Springer Verlag, New York, 177–191.CrossRefGoogle Scholar
Faber, W., Leone, N. & Pfeifer, G. 2001. Experimenting with heuristics for answer set programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI) 2001, Seattle, WA, Morgan Kaufmann, Washington, DC, 635–640.Google Scholar
Faber, W., Leone, N. & Pfeifer, G. 2004. Recursive aggregates in disjunctive logic programs: Semantics and complexity. In Proc. of JELIA 2004. 200–212.CrossRefGoogle Scholar
Faber, W. & Pfeifer, G. 1996. DLV homepage. URL: http://www.dlvsystem.com/. Access date: 30/10/2006.Google Scholar
Gallaire, H., Minker, J. & Nicolas, J. 1984. Logic and databases: A deductive approach. ACM Computing Surveys 16,2, 153–186.CrossRefGoogle Scholar
Garcia-Molina, H., Ullman, J. D. & Widom, J. 2000. Database System Implementation. Prentice Hall, Upper Soddle River, NJ.Google Scholar
Gebser, M., Kaufmann, B., Neumann, A. & Schaub, T. Conflict-driven answer set solving. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI) 2007, Hyderabad, India. AAAI Press, Menlo Park CA, 386–392.Google Scholar
Gelfond, M. & Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Giunchiglia, E., Lierler, Y. & Maratea, M. 2006. Answer Set Programming based on Propositional Satisfiability. Journal of Automated Reasoning (JAR) 36, 4, 345–377.CrossRefGoogle Scholar
Giunchiglia, E., Maratea, M. & Lierler, Y. 2004. SAT-based answer set programming. In American Association for Artificial Intelligence. AAAI Press, Menlo Park, CA, 61–66.Google Scholar
Grant, J. & Minker, J. 1992. The impact of logic programming on databases. Communications of the ACM 35, 3, 66–81.CrossRefGoogle Scholar
Greco, S. 2003. Binding propagation techniques for the optimization of bound disjunctive queries. IEEE Transactions on Knowledge and Data Engineering 15, 2, 368–385.CrossRefGoogle Scholar
Knuth, D. E. 1994. The Stanford GraphBase: A Platform for Combinatorial Computing. ACM Press, New York.Google Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G, Perri, S. & Scarcello, F. 2006. The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic, ACM Press, New York. URL: http://www.arxiv.org/ps/cs.AI/0211004. Access date: 30/10/2006.CrossRefGoogle Scholar
Lin, F. & Zhao, Y. 2002. ASSAT: Computing answer sets of a logic program by SAT solvers. In American Association for Artificial Intelligence. AAAI Press, Menlo Park, CA, 112–118.Google Scholar
Lin, F. & Zhao, Y. 2004. ASSAT: Computing answer sets of a logic program by SAT solvers. Artificial Intelligence 157, 1–2, 115–137.CrossRefGoogle Scholar
Loo, B., Hellerstein, J., Stoica, I. & Ramakrishnan, R. 2005. Declarative routing: extensible routing with declarative queries. In Proceedings of the ACM SIGCOMM 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Philadelphia, PA, ACM press, New York, NY, USA, 289–300.Google Scholar
Lu, J., Nerode, A. & Subrahmanian, V. 1996. Hybrid knowledge bases. IEEE Transactions on Knowledge and Data Engineering 8, 5, 773–785.CrossRefGoogle Scholar
Mumick, I., Finkelstein, S., Pirahesh, H. & Ramakrishnan, R. 1996. Magic conditions. ACM Transactions on Database Systems 21, 1, 107–155.CrossRefGoogle Scholar
Niemelä, I. & Simons, P. 1997. Smodels—An Implementation of the stable model and well-founded semantics for normal logic programs. In Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'97), Dagstuhl, Germany. Dix, J., Furbach, U. & Nerode, A., Eds. Lecture Notes in AI (LNAI), vol. 1265. Springer Verlag, New York, 420–429.CrossRefGoogle Scholar
Niemelä, I., Simons, P. & Syrjänen, T. 2000. Smodels: A system for answer set programming. In Proceedings of the 8th International Workshop on Non-Monotonic Reasoning (NMR'2000), Breckenridge, CO, Baral, C. & Truszczyński, M., Eds. Electronic proceedings; available at: http://xxx.lanl.gov/abs/cs.AI/0003033 Access date: 30/10/2006.Google Scholar
Przymusinski, T. C. 1988. On the declarative semantics of deductive databases and logic programs. In Foundations of Deductive Databases and Logic Programming, Minker, J., Ed. Morgan Kaufmann, Washington, DC, 193–216.CrossRefGoogle Scholar
Rao, P., Sagonas, K., Swift, T., Warren, D. & Friere, J. 1997. XSB: A system for efficiently computing well-founded semantics. In Proc. of 4th International Conference on Logic Programming and Non Monotonic Reasoning (LPNMR'97). Springer, LNAI, New York, 430–440.Google Scholar
Ross, K. 1990. Modular stratification and magic sets for datalog programs with negation. In Proc. of the ACM Symposium on Principles of Database Systems. Nashville, Tennessee. ACM Press, New York, 161–171.Google Scholar
Ullman, J. 1989. Principles of Database and Knowledge Base Systems. Computer Science Press. New York, NY, USA.Google Scholar
Winslett, M. 2006. Raghu Ramakrishnan speaks out. SIGMOD Record 35, 2, 77–85.CrossRefGoogle Scholar
Zaniolo, C., Ceri, S., Faloutsos, C., Snodgrass, R. T., Subrahmanian, V. S. & Zicari, R. 1997. Advanced Database Systems. Morgan Kaufmann, Washington, DC.Google Scholar