Abstract
Temporal constraints are part of the specification of many complex application frameworks includingactivities to be scheduled, real-time components, temporal data management, e-commerce applications, and workflow management. This paper investigates the problem of creatingan abstract view over a set of temporal constraints that may have been specified in terms of different granularities. The level of abstraction is determined by a specific time granularity chosen by the user amongthe ones in the system or created on purpose. An expressive formal model for time granularities is assumed including common granularities like hours and days as well as non-standard granularities like business days and academic semesters. The view derivation is based on a conversion technique exploitingthe periodicity of time granularities.
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References
C. Bettini, S. Ruffini. Granularity conversion of quantitative temporal constraints. DSI Technical Report N. 276-02, University of Milan, Italy, 2002.
C. Bettini, X. S. Wang, S. Jajodia. Temporal Reasoningfor SupportingW orkflow Systems. Distributed and Parallel Databases, 11(3):269–306, Kluwer, 2002.
C. Bettini, X. Wang, S. Jajodia. A general framework for time granularity and its application to temporal reasoning. Annals of Mathematics and Artificial Intelligence, 22(1,2):29–58, Kluwer, 1998.
C. Bettini, X. Wang, J. Lin, S. Jajodia, Discovering Frequent Event Patterns With Multiple Granularities in Time Sequences. IEEE Transactions on Knowledge and Data Engineering, 10(2):222–237, 1998.
R. Chandra, A. Segev, and M. Stonebraker, Implementing calendars and temporal rules in next generation databases, in Proc. of ICDE, 1994, pp. 264–273.
C. Combi, A. Montanari, Data Models with Multiple Temporal Dimensions: Completingthe Picture, in Proc. of CAiSE, pp. 187–202, 2001.
C. Combi, L. Chittaro, Abstraction on clinical data sequences: an object-oriented data model and a query language based on the event calculus Artificial Intelligence in Medicine, 17(3): 271–301, Elsevier, 1999.
T. Dean, Usingtemp oral hierarchies to efficiently maintain large temporal databases Journal of the ACM, 36(4):687–718, 1989.
R. Dechter, I. Meiri, and J. Pearl, Temporal constraint networks, Artificial Intelligence, 49:61–95, Elsevier, 1991.
I.A. Goralwalla, Y. Leontiev, M.T. Ozsu, D. Szafron, C. Combi, Temporal Granularity: Completingthe Puzzle. Journal of Intelligent Information Systems, 16(1): 41–63, Kluwer, 2001.
J.R. Hobbs, Granularity, in Proc. of IJCAI, Los Angeles, CA, 1985, pp. 432–435.
B. Le ban, D. Mcdonald, and D. Foster, A representation for collections of temporal intervals, in Proc. of AAAI, 1986, pp. 367–371.
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© 2002 Springer-Verlag Berlin Heidelberg
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Bettini, C., Ruffini, S. (2002). Deriving Abstract Views of Multi-granularity Temporal Constraint Networks. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2002. Lecture Notes in Computer Science, vol 2453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46146-9_45
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DOI: https://doi.org/10.1007/3-540-46146-9_45
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