Integrating E-Commerce and Data Mining: Architecture and Challenges
<|reference_start|>Integrating E-Commerce and Data Mining: Architecture and Challenges: We 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.<|reference_end|>
@article{ansari2000integrating,
    title={Integrating E-Commerce and Data Mining: Architecture and Challenges},
    author={Suhail Ansari, Ron Kohavi, Llew Mason, and Zijian Zheng},
    journal={WEBKDD'2000 workshop: Web Mining for E-Commerce -- Challenges and
  Opportunities},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0007026},
    primaryClass={cs.LG cs.AI cs.CV cs.DB}
}