In this paper, we describe a hedge algebras based approach to modelling uncertainty in fuzzy object-oriented databases. Membership value reflects the degree of fuzziness existing in the data values and uncertainty is extended to the class definition level and is the basis for the determination of the membership of an object in a class. | Journal of Computer Science and Cybernetics, , (2015), 277–289 DOI: DEFINITION MEMBERSHIP FUNCTION BASED ON APPROACH TO HEDGE ALGEBRAS DOAN VAN THANG1 , DOAN VAN BAN2 1 Ho Chi Minh City Industry and Trade College 2 Duy Tan University Abstract. In this paper, we describe a hedge algebras based approach to modelling uncertainty in fuzzy object-oriented databases. Membership value reflects the degree of fuzziness existing in the data values and uncertainty is extended to the class definition level and is the basis for the determination of the membership of an object in a class. On this basis, we recommend methods of determining the membership degree on characteristics of fuzzy attributes, object/class, class/superclass, and in addition, multiple inheritance was discussed and analyzed. Keywords. Fuzzy object-oriented database, hedge algrebra. 1. INTRODUCTION The relational database model and fuzzy object-oriented database (FOODB) model and related problems have been extensively researched in recent years by many domestic and foreign authors [1–15]. To perform fuzzy information in the data model, there are several basic approaches: the model based on similarity relation and the model based on possibility distribution, etc. All above approaches aim to achieve and process the fuzzy values to build valuation and comparison methods among them to manipulate data more flexibly and accurately. Based on the advantages of the structure of hedge algebra (HA) [7, 8], the authors studied the relational database model [9–15] and and fuzzy object-oriented database model [2–6] based on the approach of HA, in which linguistic semantics be quantified by quantitative semantic mapping of hedge algebra. In this approach, language semantics can be expressed in a neighborhood of intervals determined by the fuzziness measure of linguistic values of an attribute as a linguistic variable. As well as fuzzy database model, in the fuzzy object oriented .
