A METHODOLOGY FOR DATABASE AND DOCUMENT SELECTION

Abstract

As web users are facing the problems of information overload and drowning due to the significant and rapid growth in the amount of information and the number of users so there is need to provide Web users the more exactly needed information which is becoming a critical issue in web-based information retrieval and Web applications. In this work, we aspire to improve the performance of Web information retrieval and Web presentation through developing and employing Web data mining paradigms. Every search engine has a corresponding database that defines the set of documents that can be searched by the search engine. Generally, an index for all documents in the database is created and stored in the search engine. Text data in the Internet can be partitioned into several databases naturally. Proficient retrieval of preferred data can be attained if we can exactly predict the usefulness of each database, because with such information, we only need to retrieve potentially useful documents from useful databases. For a given query ‘q’ the usefulness of a text database is defined to be the no. of documents in the database that are sufficiently relevant to the query ‘q’. In this paper, we propose new approaches for database selection and documents selection. We also implement these algorithms using .net framework. Our experimental results indicate that these methods can yield substantial improvements over existing techniques.