Implementing imperfect information in fuzzy databases

Information in real-world applications is often vague, imprecise and uncertain. Ignoring the inherent imperfect nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several substantial information, which may be very useful in several data-intensive applications. In database context, several fuzzy database models have been proposed. In these works, fuzziness is introduced at different levels. Common to all these proposals is the support of fuzziness at the attribute level. This paper proposes first a rich set of data types devoted to model the different kinds of imperfect information. The paper then proposes a formal approach to implement these data types. The proposed approach was implemented within a relational object database model but it is generic enough to be incorporated into other database models.ou

Representing fuzzy decision tables in a fuzzy relational database environment.

In this paper the representation of decision tables in a relational database environment is discussed. First, crisp decision tables are defined. Afterwards a technique to represent decision tables in a relational system is presented. Next, fuzzy extensions are made to crisp decision tables in order to deal with imprecision and uncertainty. As a result, with crisp decision tables as special cases fuzzy decision tables are defined which include fuzziness in the conditions as well as in the actions. Analogous to the crisp case, it is demonstrated how fuzzy decision tables can be stored in a fuzzy relational database environment. Furthermore, consultation of these tables is discussed using fuzzy queries.Decision making;

Combining quantifications for flexible query result ranking

Databases contain data and database systems governing such databases are often intended to allow a user to query these data. On one hand, these data may be subject to imperfections, on the other hand, users may employ imperfect query preference specifications to query such databases. All of these imperfections lead to each query answer being accompanied by a collection of quantifications indicating how well (part of) a group of data complies with (part of) the user's query. A fundamental question is how to present the user with the query answers complying best to his or her query preferences. The work presented in this paper first determines the difficulties to overcome in reaching such presentation. Mainly, a useful presentation needs the ranking of the query answers based on the aforementioned quantifications, but it seems advisable to not combine quantifications with different interpretations. Thus, the work presented in this paper continues to introduce and examine a novel technique to determine a query answer ranking. Finally, a few aspects of this technique, among which its computational efficiency, are discussed