Abstract
Database support of time-varying phenomena typically assumes that entities change in a linear fashion. Many phenomena, however, change cyclically over time. Examples include monsoons, tides, and travel to the workplace. In such cases, entities may appear and disappear on a regular basis or their attributes or location may change with periodic regularity. This paper introduces an approach for modeling cycles based on cyclic intervals. Intervals are an important abstraction of time, and the consideration of cyclic intervals reveals characteristics about these intervals that are unique from the linear case. This work examines binary cyclic relations, distinguishing sixteen cyclic interval relations. We identify their conceptual neighborhood graph, showing which relations arc most similar and demonstrating that this set of sixteen relations is complete. The results of this investigation provide the basis for extended data models and query languages that address cyclically varying phenomena.
This work was partially supported by the National Science Foundation for the National Center for Geographic Information and Analysis under NSF grant number SBR-9700465 and the National Imagery and Mapping Agency under grant numbers NMA202-97-1-1023 and NMA202-97-1-1020. Max Egenhofer’s research is further supported through NSF grants IRI-9613646, BDI-9723873 and IIS-9970123; the National Institute of Environmental Health Sciences, NIH, under grant number 1 R 01 ES09816-01; Bangor Hydro-Electric Co.; and a Massive Digital Data Systems contract sponsored by the Advanced Research and Development Committee of the Community Management Staff.
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Hornsby, K., Egenhofer, M.J., Hayes, P. (1999). Modeling Cyclic Change. In: Chen, P.P., Embley, D.W., Kouloumdjian, J., Liddle, S.W., Roddick, J.F. (eds) Advances in Conceptual Modeling. ER 1999. Lecture Notes in Computer Science, vol 1727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48054-4_9
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