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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.

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 2006 Awards Committee

• Ramasamy Uthurusamy (GM, USA), Chair
• Rakesh Agrawal (Microsoft, USA)
• Eibe Frank (U. of Waikato, New Zealand)
• Jerome Friedman (Stanford, USA)
• Jiawei Han (U. Illinois Urbana-Champaign, USA)
• Geoff Webb (Monash U., Australia)
• Ian H. Witten (U. of Waikato, New Zealand)
• Xindong Wu (U. Vermont, USA)