Features

ACM SIGKDD Innovations Award is the premiere technical award in Data Mining and Knowledge Discovery field, a "Nobel Prize" of data mining, and is given to one individual or one group 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.

Previous winners of SIGKDD Innovations Award were Rakesh Agrawal (2000) and Jerome Friedman (2002).

The winner of the SIGKDD 2003 Innovations Award is Professor Heikki Mannila, Professor (Helsinki University of Technology) and Research Director, HIIT Basic Research Unit, University of Helsinki & Helsinki University of Technology, Finland. The award carries with it a memorial plaque and a check for $2,500.

Professor Mannila has the rare virtue of being able to identify new problems, viewpoints, and concepts, and thereby taking the field forward. For example, he introduced the concept of "inductive databases" that integrate data mining and databases (CACM 96). This idea is gaining considerable momentum, especially in Europe. Another example is his KDD 96 paper where he elegantly showed that frequent itemsets, samples, and the data cube could all be viewed as instantiations of a general notion of condensed representations, and that this condensed representation could then be used to get approximate confidences of arbitrary boolean rules.

Equally impressive are Professor Mannila's contributions in providing a substantial and much needed theoretical foundation in a very young field. He has given strong theoretical results for many data mining problems, including association rules and frequent time sequences. An excellent example is his work on level-wise search and borders of theories in data mining (DMKD Journal 1997). In this work, he demonstrates how the size of the border is an important factor in the complexity of the level-wise algorithm, and also shows that the problem of computing the border is tightly connected to the well-known problem of computing transversals of hypergraphs.

Professor Mannila has also made many contributions on algorithms for solving data mining problems. His seminal KDD 94 paper identified the monotonicity property for pruning candidate itemsets that underlies most current association rule mining algorithms. His pioneering work in bio-informatics includes the development of novel algorithms for identifying block structures in the human genome, and methods for gene expression analysis for various types of cancer. Other recent highlights include topics on 0-1 data, global and local models, and a large variety of novel methods for analysis of time series and sequence data.

Finally, the breadth of Prof Mannila's work is quite spectacular. He has over 130 articles in journals and refereed conferences covering such diverse topics as association rules, probabilistic modeling, inductive databases, similar time series, and bio-informatics.

Professor Mannila has been very active in the KDD community. He has served as Editor-in-Chief of the Data Mining and Knowledge Discovery journal since its creation. He is also an associate editor of ACM Transactions on Internet Technology, an action editor for Journal of Machine Learning Research, and an area editor for IEEE TKDE. He was the Program Co-Chair for KDD 97, SIAM 2002, and ECML/PKDD 2002; and an Area Chair for ICML 2001. He has also served on the KDD Steering Committee, and as SIGKDD Awards Committee Chair. Finally, he has co-authored the book "Principles of Data Mining".

On behalf of SIGKDD 2003 Awards committee,

Gregory Piatetsky-Shapiro

SIGKDD 2003 Awards committee

• Rakesh Agrawal (IBM)
• Thomas Dietterich (Oregon State University)
• Jerome Friedman (Stanford)
• Jiawei Han (U. Illinois Urbana Champaign)
• Gregory Piatetsky-Shapiro (KDnuggets), Chair
• Daryl Pregibon (AT&T)
• Foster Provost (New York University)
• Ramasamy Uthurusamy (General Motors Corp.)