A new approach of dynamic Encoded Bitmap Indexing Technique based on query history

In this paper we have proposed a new approach of encoded bitmap indexing that makes the encoded bitmap indexing technique well defined for most of the selection queries by using association rule of data mining. Furthermore, this approach makes encoded bitmap indexing (EBI) up to date after specific duration. Actually, this thesis incorporates an idea of using dynamic n-items pattern selection for encoded bitmap indexing technique lookup table to be well defined. We have used 2-items pattern selection of query predicate, 3-items pattern selection of query predicate up to n-items pattern selection of query predicate to select the most frequent pattern for the lookup table dynamically, consisting of database table attribute distinct values of character string and numbers (integer). The main feature of our work is that we have used association rule of data mining to determine the most frequent n-items pattern. It is the first time we select the n-items most frequent pattern dynamically for each attribute of the table which involved in indexing the table data.