Fuzzy Query Language Research Articles

For Fuzzy Classification of Databases with Fuzzy Classification Query Language

Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.

Horizontal fragmentation for fuzzy querying databases

Fuzzy querying is one of the main research topics of database investigators. Several research works to date focused on building fuzzy data models, fuzzy query languages, and fuzzy database systems. However, such systems turn out to be less efficient when it comes to querying very large data. Therefore, improving the performance of such database systems is an important research issue. In this paper, we address this issue by proposing a complete fragmentation methodology. Especially, we propose an horizontal fragmentation algorithm as well as different query execution strategies whose aim is minimizing the number of fragment accesses. Extensive experimental evaluation demonstrates the efficiency of our framework scaling up to millions of tuples.

An Effective Parallel XML Fuzzy Query Processing

and handling of inexactness in information has become the major issues in modern database system and next generation information systems. In order to deal with the information inexactness, fuzzy logic is integrated with various database model and theories. This paper presents a query processing model could coupled with fuzzy logic in XML database system. Our system is based on traditional XML databases, while permitting the storage of fuzzy data as well as crisp data. Crisp data are the usual precise data handled by the traditional databases whereas fuzzy logic gives the output in certain range. In this paper we are dealing with the concept of critical architectural component named fuzzy meta- knowledge base. The main aim of fuzzy meta- knowledge basis to keep the different types of fuzzy divisions for database attributes. Fuzzy meta- knowledge base defines and demonstrates data of fuzzy nature is stored in the fuzzy meta- knowledge base. The fuzzy query language is based on X-PATH. It can accept any type of fuzzy expressions in any condition in query part. For improving the performance of X-PATH, we are using Parallel Path Stack algorithm. Parallel Path Stack algorithm speed XML Query processing performance significantly. KeywordsFuzzy logic, X-PATH, fuzzy query, Fuzzy meta-

On four noncommutative fuzzy connectives and their axiomatization

In most of the query languages, conjunctive and disjunctive combinations of conditions remain the usual way for aggregation. Fuzzy query languages also offer trade-off operators, such as means in order to compensate between elementary conditions. In this paper, we would like to introduce a new type of condition basically founded on the interaction between two predicates, thus enriching the panoply of tools the user is provided with and the power of query languages.

Determination of Adequate Fuzzy Inequalities for their Usage in Fuzzy Query Languages

Although the usefulness of fuzzy databases has been pointed out in several works, they are not fully developed in numerous domains. A task that is mostly disregarded and which is the topic of this paper is the determination of suitable inequalities for fuzzy sets in fuzzy query languages. This paper examines which kinds of fuzzy inequalities exist at all. Afterwards, different procedures are presented that appear theoretically appropriate. By being applied to various examples, their strengths and weaknesses are revealed. Furthermore, an algorithm for an efficient computation of the selected fuzzy inequality is shown. Keywords—Fuzzy Databases, Fuzzy Inequalities, Fuzzy Query Languages, Fuzzy Ranking.

Querying over fuzzy description logic

Description logics (DLs) play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty in the real world, it is essential to extend DLs to deal with these vague and imprecise information. We thus propose a new fuzzy DL f-DLR-Liteℱ⊓, which allows for the presence of n-ary relations and the occurrence of concept conjunction on the left land of inclusion axioms. We also suggest an improved fuzzy query language, which supports the presence of thresholds and user defined weights. We also show that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. DL f-DLR-Liteℱ⊓ can make up for the disadvantages of knowledge representation and reasoning of classic DLs, and the enhanced query language expresses user intentions more precisely and reasonably.

A model of fuzzy linguistic IRS based on multi-granular linguistic information

An important question in IRSs is how to facilitate the IRS-user interaction, even more when the complexity of the fuzzy query language makes difficult to formulate user queries. The use of linguistic variables to represent the input and output information in the retrieval process of IRSs significantly improves the IRS-user interaction. In the activity of an IRS, there are aspects of different nature to be assessed, e.g., the relevance of documents, the importance of query terms, etc. Therefore, these aspects should be assessed with different uncertainty degrees, i.e., using several label sets with different granularity of uncertainty. In this contribution, an IRS based on fuzzy multi-granular linguistic information and a method to process the multi-granular linguistic information are proposed. The system accepts Boolean queries whose terms can be simultaneously weighted by means of ordinal linguistic values according to three semantics: a symmetrical threshold semantics, a relative importance semantics and a quantitative semantics. In the three semantics, the linguistic weights are represented by the linguistic variable “Importance”, but assessed on different label sets S 1, S 2 and S 3, respectively. The IRS evaluates weighted queries and obtains the linguistic retrieval status values of documents represented by the linguistic variable “Relevance” which is expressed on a different label set S ′.

A descriptor-based approach to OO code reuse

The COOR (Classification of Object Oriented Code for Reuse) environment exploits the advantages of OO code to promote software reuse, performing classification and analysis using a software descriptor method based on a fuzzy query language for component retrieval. Fuzzy weighting mechanisms highlight relevant features of components for reuse in specific industrial application domains.

Summary SQL — A fuzzy tool for data mining

The increasing use of computers for transactions and communication have created mountains of data that contain potentially valuable knowledge. To search for this knowledge we have to develop a new generation of tools, which have the ability of flexible querying and intelligent searching. In this paper we will introduce an extension of a fuzzy query language called Summary SQL which can be used for knowledge discovery and data mining. We show how it can be used to search for fuzzy rules.

A fuzzy query language for relational databases

A fuzzy query language (FQL) for relational databases is proposed. FQL, constructed as an enhancement of the relational domain calculus, has sufficient capability to represent all four types of the fuzzy statements distinguished by the work of L.A. Zadeh (1978). The idea for constructing FQL is in the formulation of the fuzzy matching degrees that assigns the appropriate values in the interval to any combination of fuzzy queries and tuples in relational databases. FQL helps to provide a human-oriented interface to relational databases that store a vast amount of information. Furthermore, fuzzy expert systems are expected to be provided with the facility to make use of fact data in relational databases through FQL. In addition, FQL is a theoretical basis for systematically developing a higher human-oriented interface with relational databases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.