Topological data models and approximate retrieval and reasoning

• Authors: T. Y. Lin
• Venue: CSC '89 Proceedings of the 17th conference on ACM Annual Computer Science Conference
• Year: 1989

Topological data models. One of the most important goals of data modeling is to capture the information requirements of an application in terms of structures, constrains, and operations that naturally reflect the real world situation. To date Codd's relational data modeling has been one of the most successful representations of real world problems. As requirements are extended from "(exact) retrievals" or "(exact) queries" to "approximate retrievals" or "fuzzy queries". There are needs to generalize relational data models. To capture certain semantics information, various authors introduce new structures into relational data models: Ginsburg and Hull introduced partial ordering, while Motro introduced distance. Analysis of some examples of Motro's approach reveals that the exact numeric values of distances are not important. The essence lies in the qualitative massage carried by the numeric values. In this paper, the notion of topology is used to capture the qualitative messages directly. The model is called topological data model. In terms of category theory, Codd's relational data models are representations of real world problems in the category of sets, Ginsburg and Hull's models are in the category of partial ordered sets, Motro's models are in the category of metric spaces, while present work are in the category of topological spaces.