Features

Data mining is a core technology of the information age. Its techniques are now widely used in science and industry. Thanks to the great work of previous committees and each data mining researcher and practitioner, we no longer need to preach that data mining is useful and should be taught in universities. The critical question for us now is how to move forward to achieve even greater successes. There are obviously many things that we can or should do. Below, I give just two examples:

1. Solving challenging problems: As we know, there are many very challenging research problems in the field, both old and new. Solving them can have huge impacts. We can create incentives and pool community resources to solve these problems. Some of these problems are hard because of the difficulty in obtaining real-life data and/or in evaluating the results. I propose to create a data chair to identify the right data for the right problem and to negotiate with the industry to obtain such data. We can also create a special track in KDD conferences to review papers dealing with these problems using different criteria so that they can have a good chance to be published in the KDD conference or the TKDD journal.

2. Further promoting the data mining technology: I will actively promote data mining research to funding agencies so that they can have more data mining research programs, and more data mining related interdisciplinary programs. I also plan to actively seek collaborations with our sister communities through co-location of conferences and joint programs.

During my tenure, I will constantly gather new ideas from inside and outside the community to improve SIGKDD services, KDD conferences, SIGKDD explorations and the SIGKDD website. To implement some of these ideas, I propose to set up a student volunteer body under SIGKDD so that students can help us all year round rather than just during the KDD conferences.

Finally, I would like to say that I am a hard working and responsible person. I have carried out on time all the past services to the community diligently and successfully. As chair, for any planned activity, I will personally make sure that we will not only talk about it but also do it.

Biography
Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He obtained his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC in 2002, he was with the National University of Singapore. He has published extensively in data mining, Web mining and text mining in leading conferences and journals, e.g., KDD, WWW, AAAI, IJCAI, ICML, SIGIR, and TKDE.

His research contributions include classification based on associations, interestingness in data mining (in commercial use in Motorola), learning from positive and unlabeled examples, Web data extraction (in commercial use), and opinion mining or sentiment analysis (in commercial use). He has also written a textbook titled "Web Data Mining: Exploring Hyperlinks, Contents and Usage Data".

On professional services, Liu has served as associate editors of IEEE Transactions on Knowledge and Data Engineering, and SIGKDD Explorations, and is on the editorial boards of several other journals. He also served as program chairs of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), SIAM Conference on Data Mining (SDM-2007), ACM Conference on Information and Knowledge Management (CIKM-2006), and Pacific Asia Conference on Data Mining (PAKDD-2002). In addition, he has served extensively as program committee members, senior program committee members and vices chairs of leading conferences in data mining, Web technologies and natural language progressing.

Further information about him can be found at http://www.cs.uic.edu/~liub