Abstract
Many industrial applications require the use of table constraints (e.g., in configuration problems), sometimes of significant size. During the recent years, researchers have focused on reducing space and time complexities of this type of constraint. Static and dynamic reduction based approaches have been proposed giving new compact representations of table constraints and effective filtering algorithms. In this paper, we study the possibility of combining both static and dynamic reduction techniques by proposing a new compressed form of table constraints based on frequent pattern detection, and exploiting it in STR (Simple Tabular Reduction).
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Keywords
- Compression Ratio
- Frequent Pattern
- Constraint Programming
- Deterministic Finite Automaton
- Deterministic Finite Automaton
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Bessiere, C., Régin, J.: Arc consistency for general constraint networks: preliminary results. In: Proceedings of IJCAI 1997, pp. 398–404 (1997)
Cheng, K., Yap, R.: An MDD-based generalized arc consistency algorithm for positive and negative table constraints and some global constraints. Constraints 15(2), 265–304 (2010)
Fahle, T., Schamberger, S., Sellmann, M.: Symmetry breaking. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 93–107. Springer, Heidelberg (2001)
Focacci, F., Milano, M.: Global cut framework for removing symmetries. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 77–92. Springer, Heidelberg (2001)
Gent, I.P., Jefferson, C., Miguel, I., Nightingale, P.: Data structures for generalised arc consistency for extensional constraints. In: Proceedings of AAAI 2007, pp. 191–197 (2007)
Gharbi, N., Hemery, F., Lecoutre, C., Roussel, O.: STR et compression de contraintes tables. In: Proceedings of JFPC 2013, pp. 143–146 (2013)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of SIGMOD 2000, pp. 1–12 (2000)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Hubbe, P.D., Freuder, E.C.: An efficient cross product representation of the constraint satisfaction problem search space. In: Proceedings of AAAI 1992, pp. 421–427 (1992)
Jabbour, S., Sais, L., Salhi, Y.: A mining-based compression approach for constraint satisfaction problems. CoRR, abs/1305.3321 (2013)
Jabbour, S., Sais, L., Salhi, Y., Uno, T.: Mining-based compression approach of propositional formulae. In: Proceedings of CIKM 2013, pp. 289–298 (2013)
Katsirelos, G., Bacchus, F.: Generalized nogoods in CSPs. In: Proceedings of AAAI 2005, pp. 390–396 (2005)
Katsirelos, G., Walsh, T.: A compression algorithm for large arity extensional constraints. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 379–393. Springer, Heidelberg (2007)
Lecoutre, C.: STR2: Optimized simple tabular reduction for table constraints. Constraints 16(4), 341–371 (2011)
Lecoutre, C., Likitvivatanavong, C., Yap, R.: A path-optimal GAC algorithm for table constraints. In: Proceedings of ECAI 2012, pp. 510–515 (2012)
Lecoutre, C., Sais, L., Tabary, S., Vidal, V.: Transposition Tables for Constraint Satisfaction. In: Proceedings of AAAI 2007, pp. 243–248 (2007)
Lecoutre, C., Szymanek, R.: Generalized arc consistency for positive table constraints. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 284–298. Springer, Heidelberg (2006)
Lhomme, O., Régin, J.C.: A fast arc consistency algorithm for n-ary constraints. In: Proceedings of AAAI 2005, pp. 405–410 (2005)
Mairy, J.-B., Van Hentenryck, P., Deville, Y.: An optimal filtering algorithm for table constraints. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 496–511. Springer, Heidelberg (2012)
Mohr, R., Masini, G.: Good old discrete relaxation. In: Proceedings of ECAI 1988, pp. 651–656 (1988)
Pesant, G.: A regular language membership constraint for finite sequences of variables. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 482–495. Springer, Heidelberg (2004)
Ullmann, J.R.: Partition search for non-binary constraint satisfaction. Information Science 177, 3639–3678 (2007)
Xia, W., Yap, R.H.C.: Optimizing STR algorithms with tuple compression. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 724–732. Springer, Heidelberg (2013)
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Gharbi, N., Hemery, F., Lecoutre, C., Roussel, O. (2014). Sliced Table Constraints: Combining Compression and Tabular Reduction. In: Simonis, H. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2014. Lecture Notes in Computer Science, vol 8451. Springer, Cham. https://doi.org/10.1007/978-3-319-07046-9_9
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DOI: https://doi.org/10.1007/978-3-319-07046-9_9
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