Industrial Report
Many applications are driven by evolving data. For instance, patterns in web traffic, program execution traces, network event logs, etc., are usually non-stationary. Data with evolving characteristics creates a life cycle for classifiers. Creating, updating, and recycling classifiers intelligently is important to the performance of predictive decision making. Furthermore, when resources are limited, it is a challenge to maintain good predictive power when only part of the data can be observed by the classifier. In this talk, I focus on two issues. One is how to break away from the state-of-the-art “chasing trends approach, where learning is conducted continuously, but patterns we capture is always one step behind the current trend. The other is how to decide when to spend and when to spare, with regard to limited bandwidth/CPU time in classification, for high quality decision making. I will also discuss applications that can benefit from the work. Encouraging early results scatter in many fields, including data mining, system management, network analysis, software engineering, etc., and even more challenges remain.
Haixun Wang
IBM T.J.Watson Research Center
国际会议
The Ninth International Conference on Web-Age Information Management(第九届web时代信息管理国际会议)(WAIM 2008)
张家界
英文
2008-07-20（万方平台首次上网日期，不代表论文的发表时间）