A data warehouse/online analytic processing framework for web usage mining and business intelligence reporting

Abstract

Web usage mining is the application of data mining techniques to discover usage patterns and behaviors from web data (clickstream, purchase information, customer information, etc.) in order to understand and serve e-commerce customers better and improve the online business. In this article, we present a general data warehouse/online analytic processing (OLAP) framework for web usage mining and business intelligence reporting. When we integrate the web data warehouse construction, data mining, and OLAP into the e-commerce system, this tight integration dramatically reduces the time and effort for web usage mining, business intelligence reporting, and mining deployment. Our data warehouse/OLAP framework consists of four phases: data capture, webhouse construction (clickstream marts), pattern discovery and cube construction, and pattern evaluation and deployment. We discuss data transformation operations for web usage mining and business reporting in clickstream, session, and customer levels; describe the problems and challenging issues in each phase in detail; provide plausible solutions to the issues; and demonstrate the framework with some examples from some real web sites. Our data warehouse/ OLAP framework has been integrated into some commercial e-commerce systems. We believe this data warehouse/OLAP framework would be very useful for developing any real-world web usage mining and business intelligence reporting systems. © 2004 Wiley Periodicals, Inc.