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among Implied Constraints for Two Families of Time-Series Constraints

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Principles and Practice of Constraint Programming (CP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

We consider, for an integer time series, two families of constraints restricting the max, and the sum, respectively, of the surfaces of the elements of the sub-series corresponding to occurrences of some pattern. In recent work these families were identified as the most difficult to solve compared to all other time-series constraints. For all patterns of the time-series constraints catalogue, we provide a unique per family parameterised \(\textsc {among}\) implied constraint that can be imposed on any prefix/suffix of a time-series. Experiments show that it reduces both the number of backtracks/time spent by up to 4/3 orders of magnitude.

E. Arafailova is supported by the EU H2020 programme under grant 640954 for the GRACeFUL project. N. Beldiceanu is partially supported by GRACeFUL and by the Gaspard-Monge programme. H. Simonis is supported by Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2289 and SFI/10/IN.1/I3032.

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References

  1. Akşin, O.Z., Armony, M., Mehrotra, V.: The modern call center: a multi-disciplinary perspective on operations management research. Prod. Oper. Manag. 16(6), 665–688 (2007)

    Google Scholar 

  2. Alur, R., D’Antoni, L., Deshmukh, J.V., Raghothaman, M., Yuan, Y.: Regular functions and cost register automata. In: 28th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2013, New Orleans, LA, USA, 25–28 June 2013, pp. 13–22. IEEE Computer Society (2013)

    Google Scholar 

  3. Alur, R., Fisman, D., Raghothaman, M.: Regular programming for quantitative properties of data streams. In: Thiemann, P. (ed.) ESOP 2016. LNCS, vol. 9632, pp. 15–40. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49498-1_2

    Chapter  Google Scholar 

  4. Arafailova, E., Beldiceanu, N., Carlsson, M., Flener, P., Francisco Rodríguez, M.A., Pearson, J., Simonis, H.: Systematic derivation of bounds and glue constraints for time-series constraints. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 13–29. Springer, Cham (2016). doi:10.1007/978-3-319-44953-1_2

    Chapter  Google Scholar 

  5. Arafailova, E., Beldiceanu, N., Douence, R., Carlsson, M., Flener, P., Rodríguez, M.A.F., Pearson, J., Simonis, H.: Global constraint catalog, volume ii, time-series constraints. CoRR, abs/1609.08925 (2016)

    Google Scholar 

  6. Arafailova, E., Beldiceanu, N., Douence, R., Flener, P., Francisco Rodríguez, M.A., Pearson, J., Simonis, H.: Time-series constraints: improvements and application in CP and MIP contexts. In: Quimper, C.-G. (ed.) CPAIOR 2016. LNCS, vol. 9676, pp. 18–34. Springer, Cham (2016). doi:10.1007/978-3-319-33954-2_2

    Google Scholar 

  7. Beldiceanu, N., Carlsson, M., Douence, R., Simonis, H.: Using finite transducers for describing and synthesising structural time-series constraints. Constraints 21(1), 22–40 (2016). Journal Fast Track of CP 2015. Summary. LNCS, vol. 9255, p. 723. Springer, Berlin (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Beldiceanu, N., Carlsson, M., Petit, T.: Deriving filtering algorithms from constraint checkers. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 107–122. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30201-8_11

    Chapter  Google Scholar 

  9. Beldiceanu, N., Contejean, E.: Introducing global constraints in CHIP. Math. Comput. Model. 20(12), 97–123 (1994)

    Article  MATH  Google Scholar 

  10. Beldiceanu, N., Ifrim, G., Lenoir, A., Simonis, H.: Describing and generating solutions for the EDF unit commitment problem with the ModelSeeker. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 733–748. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40627-0_54

    Chapter  Google Scholar 

  11. Bessière, C., Coletta, R., Hébrard, E., Katsirelos, G., Lazaar, N., Narodytska, N., Quimper, C.-G., Walsh, T.: Constraint Acquisition via Partial Queries. In: IJCAI, Beijing, China, p. 7, June 2013

    Google Scholar 

  12. Bessière, C., Coletta, R., Petit, T.: Learning implied global constraints. In: IJCAI 2007, Hyderabad, India, pp. 50–55 (2007)

    Google Scholar 

  13. Bessière, C., Hébrard, E., Hnich, B., Kiziltan, Z., Walsh, T.: Among, common and disjoint constraints. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds.) CSCLP 2005. LNCS (LNAI), vol. 3978, pp. 29–43. Springer, Heidelberg (2006). doi:10.1007/11754602_3

    Chapter  Google Scholar 

  14. Colcombet, T., Daviaud, L.: Approximate comparison of functions computed by distance automata. Theory Comput. Syst. 58(4), 579–613 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  15. Demassey, S., Pesant, G., Rousseau, L.-M.: A cost-regular based hybrid column generation approach. Constraints 11(4), 315–333 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Eeckhout, L., De Bosschere, K., Neefs, H.: Performance analysis through synthetic trace generation. In: 2000 ACM/IEEE International Symposium Performance Analysis Systems Software, pp. 1–6 (2000)

    Google Scholar 

  17. Kegel, L., Hahmann, M., Lehner, W.: Template-based time series generation with loom. In: EDBT/ICDT Workshops 2016, Bordeaux, France (2016)

    Google Scholar 

  18. 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). doi:10.1007/978-3-540-30201-8_36

    Chapter  Google Scholar 

  19. Picard-Cantin, É., Bouchard, M., Quimper, C.-G., Sweeney, J.: Learning parameters for the sequence constraint from solutions. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 405–420. Springer, Cham (2016). doi:10.1007/978-3-319-44953-1_26

    Chapter  Google Scholar 

  20. Marcel Paul Schützenberger: On the definition of a family of automata. Inf. Control 4, 245–270 (1961)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Ekaterina Arafailova .

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Arafailova, E., Beldiceanu, N., Simonis, H. (2017). among Implied Constraints for Two Families of Time-Series Constraints. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-66158-2_3

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