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A data warehouse/online analytic processing framework for web usage mining and business intelligence reporting
Author(s) -
Hu Xiaohua,
Cercone Nick
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20012
Subject(s) - online analytical processing , data warehouse , clickstream , computer science , business intelligence , web mining , web intelligence , data stream mining , web analytics , data science , data transformation , business analytics , database , data mining , web page , world wide web , web modeling , business analysis , business model , business , web api , marketing
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.