Semantic data modeling of spatiotemporal database applications

Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and uncertainty and fuzziness in various abstract levels. The semantic data model that we introduce in this paper utilizes unified modeling language (UML) for handling spatiotemporal information, uncertainty, and fuzziness especially at the conceptual level of database design. An environmental information system (EIS) application is used to illustrate our modeling approach and extension made to UML. By incorporating uncertainty and fuzziness into the semantic data model of a spatiotemporal EIS database application, one can handle pollution summary, analysis, and even pollution predictions, in addition to the other common uses of a database system. (C) 2001 John Wiley & Sons, Inc.


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Citation Formats
A. Yazıcı and N. Sun, “Semantic data modeling of spatiotemporal database applications,” INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, pp. 881–904, 2001, Accessed: 00, 2020. [Online]. Available: