Integrating E-Commerce and Data Mining: Architecture and Challenges

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dc.creatorAnsari, Suhail
dc.creatorKohavi, Ron
dc.creatorMason, Llew
dc.creatorZheng, Zijian
dc.date2000-07-14
dc.date.accessioned2026-07-07T03:16:23Z
dc.date.available2026-07-07T03:16:23Z
dc.descriptionWe show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.
dc.descriptionKDD workshop: WebKDD 2000
dc.identifierhttps://arxiv.org/abs/cs/0007026
dc.identifierhttp://arxiv.org/abs/cs/0007026
dc.identifierWEBKDD'2000 workshop: Web Mining for E-Commerce -- Challenges and Opportunities
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30333
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.subjectComputer Vision and Pattern Recognition
dc.subjectDatabases
dc.subjectI.2.6;H.2.8
dc.titleIntegrating E-Commerce and Data Mining: Architecture and Challenges
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