Fuzzy Sets and Fuzzy Database Models

Abstract

Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh's fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/ EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory. IntroductionInformation is often imprecise and uncertain in many real-world applications, and many sources can contribute to the imprecision and uncertainty of data or information. Therefore, it has been pointed out that we need to learn how to manage data that is imprecise or uncertain (Dalvi and Suciu 2007).Unfortunately, the classical data management techniques such as databases and XML as introduced in Chap. 1 often suffer from their incapability of representing and manipulating imprecise and uncertain data information. On this basis, since