|Title:||Enterprise Customer Data Mining for E-Business|
|Presented by:||Usama Fayyad, Co-Founder, President & CEO, digiMine, Inc.
Neal Rothleder, Director of Analytic Technology, digiMine, Inc.
Paul Bradley, Data Mining Development Lead, digiMine, Inc.
|Abstract:||Data mining methods have their origins in a variety of fields: Statistics, Databases, Pattern Recognition, AI, Visualization, High-Performance Computing, and Information Retrieval. Successful deployment of these technologies to e-business enterprise data requires: data warehouse construction, mechanisms to efficiently update the warehouse, integration of data mining technologies, and delivery of results in a form consumable by business end-users.
In an e-business enterprise environment, the data warehouse problem is further magnified by the critical need to integrate web-log data, user profile data, product catalog information, transaction and sales data, advertising campaign data, datasets from legacy systems, etc. Once the data warehouse is in place, the next steps involve integrating analytical and data mining technology efficiently with the warehouse. A key challenge to an e-business enterprise is delivering timely, interesting, actionable results to an end-user who’s expertise is marketing, sales, business development, or merchandising rather than data mining and advanced analytics.
|Biographical Information:||Usama Fayyad is a co-founder of digiMine, Inc. and has served as President and CEO since its inception in March 2000. Prior to digiMine, Usama founded and led Microsoft Research’s Data Mining & Exploration (DMX) Group from 1995 to 2000. His work there included the development of data mining prediction components for Microsoft Site Server (Commerce Server 3.0 and 4.0). From 1989 to 1995, Usama founded the Machine Learning Systems Group and developed data mining systems for the analysis of large scientific databases at the Jet Propulsion Laboratory (JPL), California Institute of Technology. During that time he received the most distinguished excellence award from Caltech/JPL and a U.S. Government Medal from NASA. He remained affiliated with JPL as Distinguished Visiting Scientist after joining Microsoft. Usama has a Ph.D. in engineering from the University of Michigan, Ann Arbor (1991). He has served as Program Co-Chair of KDD-94 and KDD-95 and as General Chair of KDD-96 and KDD-99. Usama serves as Editor-in-Chief of the journal Data Mining and Knowledge Discovery and SIGKDD Explorations.
Neal Rothleder is Director of Analytic Technology at digiMine, Inc. His focus is on delivering powerful, scalable data mining solutions to business users in an intuitive, actionable framework. His research interests include machine learning approaches to data mining, recently focusing on making academic research work in real-world problems and incorporating domain knowledge into data mining. Prior to joining digiMine, Dr. Rothleder was a Lead Engineer with the MITRE Corporation working on research and development in data mining technologies and applications. While there, he worked on projects in network intrusion detection, aviation safety, and a variety of fraud detection scenarios. Dr. Rothleder has held adjunct faculty appointments at the University of Michigan and George Mason University. He holds a Ph.D.. and an M.S. in Computer Science and Engineering from the University of Michigan.
Paul Bradley (firstname.lastname@example.org) is Data Mining Development Lead at digiMine. His primary focus is on integrating data mining technology into digiMine’s service offering. Prior to joining digiMine, he was a researcher in the Data Management, Exploration and Mining Group at Microsoft Research. While at Microsoft Research, he worked on data mining algorithms and on data mining components in Microsoft SQL Server and Commerce Server. His research interests include classification and clustering algorithms; underlying mathematical problem formulations; and issues related to scalability. He received the Ph.D. degree from the University of Wisconsin in 1998 on the topic of mathematical programming and data mining. Paul serves as Associate Editor of SIGKDD Explorations and was KDD-2001