Unified Multidimensional Fuzzy Search Using Optimizing Query Processing

In this paper the large amount of semi structured data users access and store in personal information, there is a critical need for commercial search tools using to retrieve different types of data in a simple way. Existing tools are support IR-style ranking on the textual part of the query, but only consider structure and metadata as filtering conditions. We proposed tools Google Desktop Search tools are typically support a multidimensional fuzzy search approach that allows users to perform searches for structure and metadata conditions in addition to keyword conditions. And our techniques independently score about above three dimensions and integrate the scores into a meaningful united score. The scoring mechanism depends on the Inverse Document Frequency (IDF). We adapt existing top-k query processing algorithms and optimization to improve access structure dimension. We evaluate our scoring frame work experimentally and significantly improve search accuracy. In this we show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.