The Twelfth Annual SIGKDD International Conference on
Knowledge Discovery and Data Mining
August 20 - 23, 2006
ACM SIGKDD Innovation Award
ACM SIGKDD is pleased to announce that Ramakrishnan Srikant is the winner of its 2006 Innovation Award. Srikant is recognized for his seminal work on mining association rules and privacy preserving data mining.
The ACM SIGKDD Innovation Award is the highest technical award in the field of data mining and knowledge discovery. It is given to one individual or one group of collaborators who has made significant technical innovations in the field of Data Mining and Knowledge Discovery that have been transferred to practice in significant ways, or that have significantly influenced direction of research and development in the field.
The previous SIGKDD Innovation Award winners were Rakesh Agrawal, Jerome Friedman, Heikki Mannila, Jiawei Han, and Leo Breiman.
The award includes a plaque and a check for $2,500, to be presented at KDD-2006 (The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining) on August 20, 2006 in Philadelphia, PA. Srikant will present the Innovation Award Lecture immediately after the award presentations.
Srikant identified novel pruning techniques and data structures that made the discovery of association rules feasible. He also generalized association rules along three orthogonal dimensions: discovering associations across different levels of a hierarchy over the items; discovering temporal associations ("sequential patterns"); and discovering associations over quantitative attributes. In each case, Srikant invented pruning techniques and data structures that kept the execution times practical. Srikant also showed how to push constraints over the set of items in the discovered associations into the mining algorithms. For this body of work, Srikant was awarded the prestigious Grace Murray Hopper award in 2002, which is given to the outstanding young computer professional of the year.
Srikant has also been instrumental in developing new technologies for data mining that respect the privacy of individuals whose data is being mined. There have recently been growing concerns that data mining is too powerful and that it can impinge on consumers' privacy. The conventional wisdom has been that data mining and privacy are adversaries, and the only way to protect privacy was to restrict the use of data mining. Srikant cleverly resolved this contradiction by developing techniques for "privacy preserving data mining" that exploit the difference between the level where we care about privacy, i.e., individual data, and the level where we run data mining algorithms, i.e., aggregated data. User data is randomized to disallow recovery of anything meaningful at the individual level, while still allowing recovery of aggregate information to build mining models.
Srikant's publications have had significant impact on the research community evidenced by their very high citations. His VLDB '94 paper, describing the Apriori algorithm for mining association rules, was awarded the 10-year best paper award at the 2004 VLDB conference.
The commercial impact of Srikant's work is equally impressive. Srikant was a key architect and code contributor for IBM Intelligent Miner, a technically sophisticated data mining product. Association rules are now considered one of the three primary data mining techniques (along with classification and clustering), and are part of the standard feature list for data mining products.
Srikant has actively participated in the KDD community. He served as Program Co-Chair of SIGKDD 2001 and PAKDD 2004, Vice Chair (Data Mining Track) of WWW 2006, Deputy Chair (Data Mining Track) of WWW 2004, and Vice Chair of ICDM 2004. He is the Editor-in-Chief of SIGKDD Explorations, and Associate Editor of ACM Transactions on Internet Technology.
ACM SIGKDD is pleased to present Ramakrishnan Srikant its 2006 Innovation Award for his seminal contributions on mining association rules and privacy preserving data mining.
ACM SIGKDD Service Award
ACM SIGKDD is pleased to announce that Won Kim is the winner of its 2006 Service Award. Won Kim is recognized for his key role in founding and growing ACM SIGKDD.
ACM SIGKDD Service Award is the highest service award in the field of data mining and knowledge discovery. It is given to one individual or one group who has performed significant service to the data mining and knowledge discovery field, including professional volunteer services in disseminating technical information to the field, education, and research funding.
The previous SIGKDD Service Award winners were Gregory Piatetsky-Shapiro, Ramasamy Uthurusamy, Usama M. Fayyad, Xindong Wu, and the Weka team lead by Ian Witten and Eibe Frank
The award includes a plaque and a check for $2,500, to be presented at KDD-2006 (The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining) on August 20, 2006 in Philadelphia, PA.
Won was instrumental in creating SIGKDD in 1999. He served as Interim Chair till the first election, was elected in 2001 as the Chair and guided SIGKDD through the ACM organization as its membership grew to over 1,800. He was excellent at ensuring strong fiscal discipline for SIGKDD. He managed the annual KDD conferences such that each conference had a surplus while keeping the registration fee fixed at a low level since 1999 and maintaining high quality.
Won helped initiate the Chapters Program to extend the reach of SIGKDD via local chapters and the Innovation and Service Awards. Won also initiated the SIGKDD Curriculum Committee, realizing that training in the field of data mining sets the foundation for future generations of data mining researchers. This committee has already generated a draft of two sample data mining curricula, a foundational course and an advanced data mining course. In 2003, when controversial government projects were being equated with data mining, Won countered the wrong impressions about data mining through a letter from the SIGKDD Executive Committee that argued that data mining technology is not against privacy and civil liberties.
Won has a long history of serving the academic community. He served as Chair of ACM SIGMOD from 1989 to 1997 and was the Editor-in-Chief of ACM Transactions on Database Systems from 1992 to 2001. He received the ACM SIGMOD Contributions Award in 1998, and the ACM Distinguished Services Award in 2001. He is the Founder and Editor-in-Chief of ACM Transactions on Internet Technology (since 2000).
Won has published 4 books on database systems and object-oriented technology. He has published over 150 research and technical papers in international conferences and journals. He received the VLDB 10-year Best Paper Award in 1995, and the ACM SIGMOD Test of Time Award in 2002 (for the best paper published in SIGMOD 1992). Won was elected an ACM Fellow in 1995.
ACM SIGKDD is pleased to present Won its 2006 Service Award for his significant service and contributions to the KDD community.
Best Paper/Best Student Papers Winners/Runners-UpBest Paper: Training Linear SVMs in Linear Time, by T. Joachims Runner-up: Accessing Data Mining Results via Swap Randomization, by A. Gionis, H. Mannila, T. Mielikainen, P. Tsaparas
Best Student Paper: Very Sparse Random Projections, by P. Li, T.J. Hastie, K.W. Church Runner-up: Generating Semantic Annotations for Frequent Patterns with Context Analysis, by Q. Mei, D. Xin, H. Cheng, C. Zhai, J. Han
Student Travel Awards
The list of Student Travel Awards winners for KDD 2006 are:
Sunny Aggarwal of Indian Institute of Technology Bombay, India
Justin Brickell from The University of Texas at Austin
Gregory Buehrer from Ohio State University
Jason Davis from The University of Texas at Austin
Steven Hoi from Chinese University of Hong Kong
Yiping Ke from Hong Kong University of Science and Technology
Chao Liu from University of Illinois at Urbana-Champaign
Bo Long from SUNY Binghamton
Qiaozhu Mei from University of Illinois at Urbana-Champaign
Hanghang Tong from Carnegie Mellon University
Ivor Wai from Hong Kong University of Science and Technology
Xuerui Wang from University of Massachusetts
Raymond Chi-Wing Wong from Chinese University of Hong Kong
Dong Xin from University of Illinois at Urbana-Champaign
Jian Xu from Fudan University, Shanghai
Shipeng Yu from University of Munich, Germany
Zhiping Zeng from Tsinghua University, Beijing
Qiankun Zhao from Nanyang Technological University, Singapore