An access structure for similarity-based fuzzy databases

Date

1999-04-01

Author

Yazıcı, Adnan
Cibiceli, D

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A significant effort has been made in representing imprecise information in database models by using fuzzy set theory. However, the research directed toward access structures to handle fuzzy querying effectively is still at an immature stage. Fuzzy querying involves more complex processing than the ordinary querying does. Additionally, a larger number of tuples are possibly selected by fuzzy conditions in comparison to the crisp ones. It is obvious that the need for fast response time becomes very important when the database system deals with imprecise (fuzzy) data. The current crisp index structures are inappropriate for representing and efficiently accessing fuzzy data. At the same time, in many complex applications such as Expert Database Systems, Multimedia Database Systems, Decision Support Systems, etc., fuzzy queries are usually intermingled with crisp queries. For the effectiveness of fuzzy databases, it is necessary to allow both the non-fuzzy and fuzzy attributes to be indexed together; therefore, a multi-dimensional access structure is required. Beside a suitable access structure, an effective partitioning, representation, and storage of fuzzy data art: also necessary for efficient retrieval. In this study we utilise a multi-dimensional data structure, namely Multi Level Grid File (MLGF), for efficiently accessing both crisp and fuzzy data from fuzzy databases. Therefore, we focus on the issue of partitioning, representation and organisation of fuzzy and crisp data at physical database level, i.e., record and file structures, in addition to the design of the access structure. The implementation of the access structure is also described and its comparison with a previously proposed fuzzy access method is given along with the experimental results. (C) 1999 Elsevier Science Inc. All rights reserved.

Subject Keywords

Control and Systems Engineering, Theoretical Computer Science, Software, Information Systems and Management, Artificial Intelligence, Computer Science Applications

URI

https://hdl.handle.net/11511/62450

Collections

Department of Computer Engineering, Article