Extension of the Relational Database and Its Algebra With Rough Set Techniques

Abstract

This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes. except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be. defined for every attribute domain.There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise. or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational dardbase. In addition, applications inherently "rough" could be more easily implemented and maintained in the rough relational database.

Overview on the Using Rough Set Theory on GIS Spatial Relationships Constraint

“…Spatial data mining means to extract the information users interested in which includes common relationships of spatial patterns and features, or spatial and non-spatial data, and some other general data characteristics hidden in the database data. Accordingly spatial data shows increasing important in the found and remake nature projects of people activity, the research and application of spatial data mining also increasingly aroused concerns, and the rough set theory is one of the important methods introduced to the data mining, in 1995 Theresa Beaubouef tried to describe a database model based on the original rough sets theory, and introduced some rough relational database models which include systems involving ambiguous, imprecise, or uncertain data [27], and Wang used GIS attribute mining as an example to analyze the application of rough set in GIS data mining [28].…”

Ai and Intelligent Industrial Applications: The Rough Set Perspective

“…Cement kiln control algorithms obtained from observation of stoker actions and blast furnace control in iron and steel works are exemplary applications of rough set techniques in intelligent industrial control (Mrózek, 1989(Mrózek, , 1992 3) Decision support systems. Rough set based decision support systems can be widely used in many kinds of industrial decision making on various levels, stretching down from specific industrial process up to management and business decisions (Golan & Ziarko, 1995, Pawlak, 1994, Słowiński, 1992, Stepaniuk, 1996 (Arciszewski & Ziarko, 1987, signal and image processing (Kowalczyk, 1996), data bases and information retrieval (Beaubouef et al, 1995, Funakoshi & Tu Bao Ho, 1996 and others , Furuta et al, 1996, Rubin et al, 1996and Zak & Stefanowski, 1994.…”

Spatial data methods and vague regions: A rough set approach

“…The various forms of uncertainty may occur in both types of data, so many of the issues regarding uncertainty apply to ordinary databases as well. See [6,7] for in-depth discussion of incorporation of rough set uncertainty in (non-spatial) databases.…”

“…It has also been used for improved information retrieval [24] and for uncertainty management in relational databases [6,7].…”

“…In relational databases it has been demonstrated that uncertainty may be managed via rough set techniques by incorporating rough sets into the underlying data model [6] and through rough querying of crisp data [7]. In a previous work [8], we pointed out those areas peculiar to spatial databases and GIS that are in need of uncertainty management and suggested ways in which rough sets techniques may be used to alleviate the problems to result in a better overall system.…”