With the developing of Distributed Database technology, fuzzy query of Distributed Database has been playing an important role in the information query. Raising query speed and precision ratio is always the main topic of information query. In this paper, a new type of information query system--Distributed Database system is designed with rough set theory and fuzzy set knowledge. A new information query method is advanced and the architecture of the system and its key algorithms are given. Main characteristic of the research achievement is that query algorithm’s time complexity decreases with the increase of amount of information and relates to the only amount of index words. Results show that it is an effective method with which a system for fuzzy query is implemented.

Due to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. This paper presents an inverted file method to improve the effectiveness of retrieving documents. It integrates the effects of the document identifier data and document weight data to improve the efficiency of retrieving process. Moreover, we propose the Hybrid Trie-Inverted file (HTI) index because the evaluation of containment queries relies on merge-joining the inverted lists, and then we conduct several experiments to evaluate our proposed approach. The experimental results show that it can get higher performance than traditional Inverted file index.