ArcGIS: General Purpose GIS Software System
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cesses often lie skew to the cells of calendar partitions (i. e., ‘x happened yesterday’ does not mean that x started at 12 a.m. and ended at 0 p.m.) Thus, descriptions of the temporal location of events and processes are often approximate and rough in nature rather than exact and crisp. As demonstrated in [1] and [18], rough approximation and reasoning methods of the sort introduced above can be used to represent and to reason about approximate temporal location.
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Cross References
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Representing Regions with Indeterminate Boundaries Uncertainty, Semantic
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Recommended Reading 1. Bittner, T.: Approximate qualitative temporal reasoning. Annals of Mathematics and Artificial Intelligence 35(1–2), 39–80 (2002) 2. Bittner, T., Stell, J.G.: Approximate qualitative spatial reasoning. Spatial Cognition and Computation 2(4),435–466 (2002) 3. Bittner, T., Stell, J.G.: Vagueness and rough location. GeoInformatica 6, 99–121 (2002) 4. Bittner, T., Stell, J.G.: Stratified rough sets and vagueness. In: Kuhn, W., Worboys, M., Impf, S. (eds.) Spatial Information Theory. Cognitive and Computational Foundations of Geographic Information Science. International Conference COSIT’03, pp. 286–303. Springer, Berlin (2003) 5. Burrough, P., Frank, A.U. (eds.): Geographic Objects with Indeterminate Boundaries, GISDATA Series II. Taylor and Francis, London (1995) 6. Cohn, A.G., Gotts, N.M.: The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Burrough, P.A., Frank, A.U. (eds.) Geographic Objects with Indeterminate Boundaries, GISDATA Series II. pp. 171–187. Taylor and Francis, London (1996) 7. Duentsch, I., Gediga, G.: Rough set data analysis: A road to non-invasive knowledge discovery. Methodos Publishers, Bangor (2000) 8. Goodday, J.M., Cohn,A.G.: Conceptual neighborhoods in temporal and spatial reasoning. In: ECAI-94 Spatial and Temporal Reasoning Workshop (1994) 9. Hobbs, J.: Granularity. In: Proceedings of the IJCAI 85 (1985) 10. Orłowska, E.(ed.): Incomplete Information – Rough Set Analysis. Studies in Fuzziness and Soft Computing, vol.13 PhysicaVerlag, Heidelberg (1998) 11. Pawlak, Z.: Rough sets. Internat. J. Comput. Inform. 11, 341–356 (1982) 12. Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data. Theory and decision library. Series D, System theory, knowledge engineering, and problem solving, vol. 9. Kluwer Academic Publishers, Dordrecht, Boston (1991) 13. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, R.A.: Rough sets. Communications of the ACM 38(11), 89–95 (1995) 14. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. J. Approx. Reason. 15(4),333–365 (1996) 15. Polkowski, L.: A survey of recent results on spatial reasoning via rough inclusions. In: Bolc, L., Michalewicz, Z., Nishida, T. (eds.) Intelligent Media Technology for Communicative Intelligence,
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Second International Workshop, IMTCI 2004, Lecture Notes in Computer Science, Springer, Berlin (2004) Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic base
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