The Study Of Intelligent Query System Based On Computing With Words

The traditional relational DBS is based on 2-value logic and precise data tool, and it is very difficult to deal with the fuzzy and uncertain information . So, DBMS existing could not process query expressed with natural language.Generally speaking, two methods are involved in gaining the results of fuzzy query. One is to set up a fuzzy DBMS pre-storing fuzzy data, and the other method is to extend SQL by transferring conditional sentences based on the theory of fuzzy logic and the present relation DBS pattern,that is the model of relation database. Thus it can deal with fuzzy query terms, carry out the precise SQL match after transferring the fuzzy calculation to a fuzzy scope. This method is relatively simple and closely connected with the common inquiry. Paper realizes the fuzzy query with weight through fuzzy extension of SELECT sentences in SQL language. But the given weigth is casually administrator-appointed precise data without much consideration to the complicities and uncertainties of objective things as well as the fuzzyness of human thoughts. Therefore the weight does not always fit the reality.Computing with words is a brand-new method of intelligent information process. It is based calculus of linguistically quantified propositions and imitating procedure of brain thinking. CWW has developed very well and played an important role in many research domains ,such as: fuzzy control, knowledge engineering, pattern recognition, etc.This thesis makes it practical for the fuzzy query in the part of language data of DBS by using CWW(Computing with Words) theory and putting the examples of personnel information fuzzy query. It offers a way to set threshold and weight for query terms and to calculate weight by connecting linguistic aggregation operator without the knowledge of weight. As a result, the examples verify the effectiveness of methods mentioned this thesis and show the potential of application in this field.