Journal of Intelligent Information Systems

, Volume 34, Issue 1, pp 57–92 | Cite as

Using temporal constraints for temporal abstraction

  • M. CamposEmail author
  • J. M. Juárez
  • J. Palma
  • R. Marín


The need to provide high level descriptions of the evolution of data is evident in fields like medicine. For being able to perform task such as diagnostic or monitoring, it is very important to facilitate a high level representation and management of temporal data. With this representation two main benefits are obtained: it becomes easier to compare data with generic knowledge, and the volume of data can be reduced. Several models have been proposed for time representation and management. Temporal constraints have been extensively used as a liable model in problems where temporal imprecision or missing data exist. The imprecision is usually present when data are manually collected, or when the data are based on subjective observations. The aim of this paper is to demonstrate how temporal constraints can be used as a formalism in which temporal abstraction of concepts can be performed. To this end, in the first place, we introduce the fuzzy temporal constraint network as the formalism for representing temporal information. Then, we present an algorithm for obtaining a state representation from a sequence of observations. We show the complexity and applicability of the approach.


Temporal abstraction Temporal reasoning CSP Fuzzy temporal constraint network 


  1. Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26, 832–843.zbMATHCrossRefGoogle Scholar
  2. Anselma, L., Terenziani, P., Montani, S., & Bottrighi, A. (2006). Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines. Artificial Intelligence in Medicine, 38, 171–195.CrossRefGoogle Scholar
  3. Barro, S., Marín, R., Mira, J., & Patón, A. R. (1994). A model and a language for the fuzzy representation and handling of time. Fuzzy Sets and Systems, 61, 153–175.CrossRefMathSciNetGoogle Scholar
  4. Bellazi, R., Larizza, C., & Riva, A. (1998). Temporal abstraction for interpreting diabetic patients monitoring data. Intelligent Data Analysis, 2(1–2), 97–122.CrossRefGoogle Scholar
  5. Brusoni, V., Console, L., Terenziani, P., & Dupré, D. T. (1998). A spectrum of definitions for temporal model-based diagnosis. Artificial Intelligence, 102(1), 39–79.zbMATHCrossRefMathSciNetGoogle Scholar
  6. Combi, C., & Chittaro, L. (1999). Abstraction on clinical data sequences: And object oriented data model and a query language based on the event calculus. Artificial Intelligence in Medicine, 17(3), 271–301.CrossRefGoogle Scholar
  7. Campos, M., Cárceles, A., Palma, J., & Marín, R. (2002). A general purporse fuzzy temporal information management engine. In EurAsia-ICT 2002. Advances in information and communication technology (pp. 93–97).Google Scholar
  8. Campos, M., Juáres, J. M., Palma, J., & Marín, R. (2007). Temporal data mining with temporal constraints. In Artificial intelligence in medicine. 11th conference on artificial intelligence in medicine. LNCS (Vol. 4594, pp. 67–76).Google Scholar
  9. Campos, M., Martínez, A., Palma, J., & Marín, R. (2005). Modelo genérico de abstracción temporal de datos. In Proceedings of the XI conferencia de la asociación Espanola para la inteligencia artificial. CAEPIA05 (Vol. 2, pp. 51–60).Google Scholar
  10. DeCoste, D. (1991). Dynamic across-time measurement interpretation. Artificial Intelligence, 51(1), 273–341.CrossRefGoogle Scholar
  11. Dojat, M., Ramaux, N., & Fontaine, D. (1998). Scenario recognition for temporal reasoning in medical domains. Artificial Intelligence in Medicine, 14(1–2), 139–155.CrossRefGoogle Scholar
  12. Dubois, D., & Prade, H. (1988). Possibilistic Theory: An approach to computerized processing of uncertainty. New York: Plenum.Google Scholar
  13. Felix, P., Barro, S., & Marín, R. (2003). Fuzzy constraint networks for signal pattern recognition. Artificial Intelligence, 148(1–2), 103–140.zbMATHCrossRefMathSciNetGoogle Scholar
  14. Flach, P. A., & Kakas, A. C. (2000). Abduction and induction reasoning: Background and issues. Applied logic series (chapter 1, pp. 1–27). Dordrecht: Kluwer.Google Scholar
  15. Gamper, J., & Nejdl, W. (1997). Abstract temporal diagnosis in medical domains. Artificial Intelligence in Medicine, 10(3), 1116–1122.CrossRefGoogle Scholar
  16. Haimowitz, I. J., & Kohane, I. S. (1996). Managing temporal worlds for medical trend diagnosis. Artificial Intelligence in Medicine, 8, 299–321.CrossRefGoogle Scholar
  17. Hau, D. T., & Coiera, E. W. (1997). Learning qualitative models of dynamic systems. Machine Learning, 26(2–3), 177–211.zbMATHCrossRefGoogle Scholar
  18. Ho, T. B., Nguyen, D. D., Kawasaki, S., & Nguyen, T. D. (2002). Extracting knowledge from hepatitis data with temporal abstraction. In H. Phuong, H. T. Nguyen, N. C. Ho, & P. Santiprabhob (Eds.), Proceedings of the joint third international conference on intelligent technologies and third Vietnam–Japan symposium on fuzzy systems and applications (InTech/VJFuzzy2002) (pp. 362–370).Google Scholar
  19. Ho, T. B., Nguyen, T. D., Kawasaki, S., & Le, S. Q. (2004). Combining temporal abstraction and data mining methods in medical data mining. Intelligent Knowledge-Based Systems, 3, 198–222.Google Scholar
  20. Josephson, J., & Josephson, S. (1994). Abductive inference: Computation, philosophy, technology. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  21. Kautz, H., & Ladkin, P. (1991). Integrating metric and qualitative temporal reasoning. In Proceedings of the 9th national conference on artificial intelligence, AAAI-91 (pp. 241–246).Google Scholar
  22. Khatib, L., Morris, P. H., Morris, R. A., & Rossi, F. (2001). Temporal constraint reasoning with preferences. In Seventeenth international joint conference on artificial intelligence, IJCAI 2001 (pp. 322–327).Google Scholar
  23. Kryszkiewicz, M., Rybinski, H., & Gajek, M. (2004). Dataless transitions between concise representations of frequent patterns. Journal of Intelligent Information System, 22(1), 41–70.CrossRefGoogle Scholar
  24. Lavrač, N., Keravnou, E. A, & Zupan, B. (2000). Intelligent data analysis in medicine. Encyclopedia of computer science and technology (Vol. 42, pp. 113–157). New York: Marcel Dekker.Google Scholar
  25. Marín, R., Barro, S., Palacios, F., Ruiz, R., & Martín, F. (1994a). An approach to fuzzy temporal reasoning in medicine. Mathware & Soft Computing, 1(3), 265–276.Google Scholar
  26. Marín, R., Cárdenas, M. A., Balsa, M., & Sánchez, J. L. (1996). Obtaining solutions in fuzzy constraint networks. International Journal of Approximate Reasoning, 16(3–4), 261–288.Google Scholar
  27. Marín, R., Mira, J., Patón, R., & Barro, S. (1994b). A model and a language for the fuzzy representation and handling of time. Fuzzy Sets and Systems, 61, 153–165.CrossRefMathSciNetGoogle Scholar
  28. Meiri, I. (1996). Combining qualitative and quantitative constraints in temporal reasoning. Artificial Intelligence, 87(1–2), 343–385.CrossRefMathSciNetGoogle Scholar
  29. Miksch, S., Horn, W., Popow, C., & Paky, F. (1996). Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants. Artificial Intelligence in Medicine, 8(6), 543–576.CrossRefGoogle Scholar
  30. Miksch, S., Seyfang, A., Horn, W., & Popow, C.(1999). Abstracting steady qualitative descriptions over time from noisy, high-frequency data. In W. Horn, Y. Shahar, G. Lindberg, S. Andreassen, & J. Wyatt (Eds.), Artificial intelligence in medicine. Joint European conference on artificial intelligence in medicine and medical decision making, AIMDM’99. Lecture notes in computer science (Vol. 1620, pp. 281–290). New York: Springer.Google Scholar
  31. Nguyen, J. H., Shahar, Y., Tu, S. W., Das, A. K., & Musen, M. A. (1999). Integration of temporal reasoning and temporal-data maintenance into a reusable database mediator to answer abstract, time-oriented queries: The tzolkin system. Journal of Intelligent Information System, 13(1–2), 121–145.CrossRefGoogle Scholar
  32. Palma, J., Juárez, J. M., Campos, M., & Marín, R. (2006). Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains. Artificial Intelligence in Medicine, 38(2), 197–218.CrossRefGoogle Scholar
  33. Perkins, W., & Austin, A. (1990). Adding temporal reasoning to expert-system building environments. IEEE Expert, 5(1), 23–30.CrossRefGoogle Scholar
  34. Salatian, A. (2003). Interpreting historical ICU data using associational and temporal reasoning. In 15th IEEE international conference on tools with artificial intelligence (ICTAI 2003) (pp. 442–451). Sacramento: USA.Google Scholar
  35. Seyfang, A., & Miksch, S. (2004). Advanced temporal data abstraction for guideline execution. In K. Kaiser, S. Miksch, & S. Tu (Eds.), Symposium on computerized guidelines and protocols: Computer-based support for clinical guidelines and protocols (CGP 2004) (pp. 88–102). Prague: IOS.Google Scholar
  36. Seyfang, A., Miksch, S., Horn, W., Urschitz, M. S., Popow, C., & Poets, C. F. (2001). Using time-oriented data abstraction methods to optimize oxygen supply for neonates. In Proceedings of European conference on artificial intelligence in medicine (AIME 2001) (pp. 217–226). Cascais: Portugal.Google Scholar
  37. Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1–2), 79–133.zbMATHCrossRefGoogle Scholar
  38. Shahar, Y. (1998). Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence, 22(1–2), 159–192.zbMATHCrossRefGoogle Scholar
  39. Shahar, Y., & Musen, M. (1993). RÉSUMÉ: A temporal-abstraction system for patient monitoring. Computers and Biomedical Research, 26, 255–273.CrossRefGoogle Scholar
  40. Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267–298.CrossRefGoogle Scholar
  41. Shoham, Y., & McDermott, D. (1988). Problems in formal temporal reasoning. Artificial Intelligence, 36, 49–61.zbMATHCrossRefGoogle Scholar
  42. Russ, T. A. (1995). Use of data abstraction methods to simplify monitoring. Artificial Intelligence in Medicine, 7(6), 497–514.CrossRefGoogle Scholar
  43. van Beek, P. (1991). Temporal query processing with indefinite information. Artificial Intelligence in Medicine, 3(6), 325–339.CrossRefGoogle Scholar
  44. Vila, L., & Godo, L. (1994). On fuzzy temporal constraint networks. Mathware and Soft Computing, 1(3), 315–334.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • M. Campos
    • 1
    Email author
  • J. M. Juárez
    • 2
  • J. Palma
    • 2
  • R. Marín
    • 2
  1. 1.Informatics and Systems Dept., Computer Science facultyUniversity of MurciaMurciaSpain
  2. 2.Information and Communications Engineering Dept., Computer Science facultyUniversity of MurciaMurciaSpain

Personalised recommendations