Keyword Based Searching According to the Movie Names

Keyword based queries are inherently ambiguous such that given a set of keywords the database search engine has only an uncertain guess about the user's informational need represented by the query. Possibly high complexity of the data makes providing intelligent search results effectively extremely challenging. Databases enable users to precisely express their informational needs using structured queries. However, database query construction is a laborious and error-prone process, which cannot be performed well by most end users. Keyword search alleviates the usability problem at the price of query expressiveness. As keyword search algorithms do not differentiate between the possible informational needs represented by a keyword query, users may not receive adequate results. This paper presents Extended Incremental Query Processing - a novel approach to bridge the gap between usability of keyword search and expressiveness of database queries. Extended Incremental Query Processing enables a user to start with an arbitrary keyword query and incrementally refine it into a structured query through an interactive interface. The enabling techniques of Extended Incremental Query Processing include: 1) A probabilistic framework for incremental query construction; 2) A probabilistic model to assess the possible informational needs represented by a keyword query; 3) An algorithm to obtain the optimal query construction process. This paper presents the detailed design of Extended Incremental Query Processing, and demonstrates its effectiveness and scalability through experiments over real-world data and a user study. Extracting information from semi structured documents is a very hard task. Documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intentional information on both the structure and contents of XML documents. This mined knowledge is later used to provide: a concise idea—the gist—of both the structure and the content of the XML document .quick, approximate answers to queries.