Scalable Web mining architecture for backward induction in data warehouse environment

Abstract

For Web mining, the biggest problem is the scarcity of data. To overcome the problem and prepare as much needed data as possible for business intelligent information, we propose backward induction in Web mining. Web mining itself is an iterative process where data mining techniques are used back and forth and iteratively. To support backward induction and Web mining characteristics, the scalable Web mining architecture in a data warehouse environment is proposed. The proposed Web mining architecture has three kinds of scalabilities. These are: the scalabilities of operational database, the scalabilities of data model and the scalabilities of data mining engines. By implementing the scalable Web mining architecture with three kinds of scalabilities in a data warehouse environment to support backward induction procedures, we can extract business intelligent information from Web mining.