Flexible Query Answering Systems 

About: Flexible Query Answering Systems is an academic conference. The conference publishes majorly in the area(s): Fuzzy logic & Query language. Over the lifetime, 530 publications have been published by the conference receiving 4515 citations.

A Server for Fuzzy SQL Queries

Abstract: The client-server model is being used mostly in the actual DataBase Management Systems (DBMS). However, these DBMS do not allow either to make flexible queries to the database or to store vague information in it. We have developed a FSQL Server for a Fuzzy Relational Database (FRDB). The FSQL language (Fuzzy SQL) is an extension of the SQL language that allows us to write flexible conditions in our queries. This Server has been developed for Oracle, following the model GEFRED, a theoric model for FRDB that includes fuzzy attributes to store vague information in the tables. The FSQL Server allows us to make flexible queries about traditional (crisp) or fuzzy attributes and we can use linguistic labels defined on any attribute.

Bipolarity in Flexible Querying

Abstract: The paper advocates the interest of distinguishing between negative and positive preferences in the processing of flexible queries. Negative preferences express what is (more or less, or completely) impossible or undesirable, and by complementation state flexible constraints restricting the possible or acceptable values. Positive preferences are not compulsory, but rather express wishes; they state what attribute values would be really satisfactory. The paper discusses the handling of bipolar queries, i.e. queries involving negative and positive preferences, in the framework of possibility theory. Both ordinary queries expressed in terms of requirements, and case-based queries referring to examples, are considered in this perspective.

Using fuzzy sets in flexible querying: why and how?

Abstract: The last few years have witnessed a tremendous increase in the use of computers in more and more domains, the need for managing new kinds of data and for providing new capabilities for storage, access and display of information. In this respect, one may imagine introducing what is often dubbed “uncertainty” ’into databases. This term may refer to two main streams of problems. On the one hand, one wants to store and manipulate incomplete data (i.e., the available information about attribute values may be tainted with imprecision and/or uncertainty for some items) . In that case, the retrieval process will also return results involving some uncertainty (if we are uncertain about the precise value of John’s age, we cannot always be sure that John does (or does not) satisfy a given requirement in the context of a query selecting people on basis of their age). On the other hand, the term “uncertainty” is sometimes (and somewhat misleadingly) used for referring to flexible queries, since one may then consider that there is some ambiguity pertaining to their meaning. In fact, flexible queries are useful for describing preferences and thus for getting an ordered set of answers accordingly.

Specifying and Querying Database Repairs using Logic Programs with Exceptions

Abstract: Databases may be inconsistent with respect to a given set of integrity constraints. Nevertheless, most of the data may be consistent. In this paper we show how to specify consistent data and how to query a relational database in such a way that only consistent data is retrieved. The specification and queries are based on disjunctive extended logic programs with positive and negative exceptions that generalize those previously introduced by Kowalski and Sadri.

Estimating the Quality of Databases

Abstract: With more and more electronic information sources becoming widely available, the issue of the quality of these often-competing sources has become germane. We propose a standard for specifying the quality of databases, which is based on the dual concepts of data soundness and data completeness. The relational model of data is extended by associating a quality specification with each relation instance, and by extending its algebra to calculate the quality specifications of derived relation instances. This provides a method for calculating the quality of answers to arbitrary queries from the overall quality specification of the database. We show practical methods for estimating the initial quality specifications of given databases, and we report on experiments that test the validity of our methods. Finally, we describe how quality estimations are being applied in the Multiplex multidatabase system to resolve cross-database inconsistencies.