Một cách tiếp cận mở rộng mô hình cơ sở dữ liệu quan hệ để xử lý thông tin không đầy đủ và các phụ thuộc dữ liệu.

Một cách tiếp cận mở rộng mô hình cơ sở dữ liệu quan hệ để xử lý thông tin không đầy đủ và các phụ thuộc dữ liệu. Do vậy, cách tiếp cận hệ thống đã được sử dụng trong lý thuyết mang tính chất liên ngành, tạo ra cơ hội đem những quy luật và những khái niệm từ một lĩnh vực nhận thức này sang một lĩnh vực khác.2. Điều khiển học thế hệ thứ hai
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Một cách tiếp cận mở rộng mô hình cơ sở dữ liệu quan hệ để xử lý thông tin không đầy đủ và các phụ thuộc dữ liệu. Ti!p chi Tin hoc va Dieu khien hoc, T.17, S.3 (2001), 41-47 AN APPROACH TO EXTENDING THE RELATIONAL DATABASE MODEL FOR HANDLING INCOMPLETE INFORMATION AND DATA DEPENDENCIES HO THUAN, HO CAM HAAbstract. In this paper we propose a new approach to extending the relational database model. Thisapproach is based on the concept of similarity based fuzzy relational database and somewhat of new viewpointon redundancy. It is shown that, in such an extended database model, we can capture imprecise, uncertaininformation. The formal definition of fuzzy functional and multivalued dependencies in this study allowsa sound and complete set of inference rules. This paper describes an ongoing work. We state some openproblems to be solved in order to render our approach more operational.T6m t~t. Bai bao de xuat mi?t each tiep c~n m&i M m& ri?ng me hlnh err s& dir li~u quan h~. Cach tiepc~n nay du-a tren khii niern err s& dir li~u mer tircng t~· va mi?t quan diem mo-i ve duo th ira dir li~u. V &i mehlnh err S6-dir li~u nhir v~y co the nitm bitt dtroc nhirng thong tin khong chinh xac, khOng chltc chan. Dinhnghia ve phu thuoc ham mer va phu thuoc da tri mer trong bai bao cho m9t t~p cac lu~t suy din xac dingva diy dii. 1. INTRODUCTION Database systems have been extensively studied since Codd [3] proposed the relational datamodel. Such database systems do not accept uncertain and imprecise data. In fact, the value of anobjects attribute may be completely unknown, incompletely known (i.e., only a subset of possiblevalues of the attribute is known) or uncertain (e.g. a probability or possibility distribution for its valueis known). In addition, the attribute may not be applicable to some of the objects being consideredand, in certain cases, we may not known whether the value even exists, or not. Many approachesto that problem have been proposed. One of them is A fuzzy representation of data for relationaldatabase [2], which is suggested by P. Buckles and E. Petry. In [2] a structure for representinginexact information in the form of a relational database is presented. The structure differs fromordinary relational database in two important respects: value of an attribute of an object need notbe single value and a similarity relation is required for each domain set of the database. In a fuzzydatabase proposed by these authors, a tuple is redundant if it can be merged with another through theset union of corresponding domain values. The merging of tuple, however, is subject to constraintson some similar thresholds. Within this conception, in a fuzzy relation with no redundant tuplesand each domain similarity relation formulated according to Tl transitivity, each tuple representsinformation of an object, and each value of an attribute (called domain value) consists of one or moreelements from the domain base set. At this point, there is an emphatic notice that elements of eachdomain value must be similar enough to each other (i.e. similarity degree of every couple of elementsis not less than the given threshold). The work reported here is quite distinct from that of P. Buckles and E. Petry in that the elementsof each domain value are not required to be similar enough according to the threshold. This ideaallows each domain value to contain elements, which even are not very similar and represent thepossibilities that can be happened. Therefore, to model a relational database by using this approachwill preserve not only the exact information but also the nuances of fuzzy uncertainty. This paper is organized as follows. Notations and basic definitions related to fuzzy relationaldata model and similarity relation, are reviewed in Section 2 to get an identical understanding ofterminology. A new definition about tuple redundant is presented in Section 3. Section 4 contains42 HO THUAN, HO CAM HAdefinition of functional dependency in this scene. The soundness and completeness of the set ofaxioms, which is similar with Amstrongs axioms in the traditional relational database, will be provedin this section. In Section 5, we propose a formal definition of fuzzy multivalued dependency and theinference rules. 2. BACKGROUND First, similarity relations are described as defined by Zadeh [9]. Then the basic concepts of fuzzyrelational database model are reviewed. Similarity relations are useful for describing how similar two elements from the same domain are.Definition 2.1. ([5]) A similarity relation, SD (x, y), for a given doma ...