SIGKDD

SIGKDD Service Award

The award recognizes individuals or teams for their outstanding services contributions to the field of knowledge discovery in data and data mining that include such professional services as the running of professional societies and conferences, educating students and professionals, funding R&D activities, etc.

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Past SIGKDD Service Award Recipients

Frequency of the Awards

Once a year.

Administration of the Awards Program

The SIGKDD Awards Committee, consisting of 3-5 prominent senior scientists in the field, will solicit nominations for recipient candidates, evaluate the nominations, and select winners.

The SIGKDD Chair will form the Awards Committee by inviting candidates. The SIGKDD Chair will appoint the Chair of the Awards Committee.

The terms of the Awards Committee will be the same as the term of the SIGKDD Chair.

Once formed, the Awards Committee will select winners of the Awards completely independently of SIGKDD Chair or SIGKDD Executive Committee.

The Award winner will be decided by a two-thirds majority vote of the Awards Committee.

There will be at most one individual or one group to receive either Award in any given year. (It is possible that in a given year, there may be no winner of either Award.)

The Awards Committee will solicit nominations for Award recipients 5 months before the SIGKDD Annual International Conference via the SIGKDD website, SIGKDD Annual Conference website, and the KDNuggets electronic newsletter.

Nominations, once made, may be re-considered for the subsequent two years; if the nominee does not win after the first three years, the nomination is discarded.

The deadline for the nominations will be 3 months before the SIGKDD Annual International Conference. (The Awards Committee will take 6 weeks to make its decisions.)

The winners will receive the Awards at the SIGKDD Annual International Conference. The winners will be announced in the SIGKDD Conference website and the SIGKDD website.

The Awards

Each Award carries a $2,500 monetary award and a plaque.

If the winner is a group of individuals, the group will receive $2,500 (not each individual). However, each individual will receive a plaque.

Exclusions

SIGKDD Chair and members of the SIGKDD Awards Committee are not eligible to be nominated for either Award.

2007 ACM SIGKDD Awards Committee

• Ramasamy Uthurusamy (General Motors, USA) Chair
• Jerome Friedman (Stanford University, USA)
• Jiawei Han (University of Illinois Urbana Champaign, USA)
• Vipin Kumar (University of Minnesota, USA)
• Heikki Mannila (University of Helsinki, Finland)
• Rajeev Motwani (Stanford University, USA)
• Ramakrishnan Srikant (Google, USA)
• Ian H. Witten (University of Waikato, New Zealand)
• Xindong Wu (University of Vermont, USA)

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Citations

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2000 Service Award: Dr. Gregory Piatetsky-Shapiro

Gregory Piatetsky-Shapiro has received the first ACM SIGKDD Service award for starting the KDD conferences and contributions to the KDD community, including KDnuggets newsletter.

Dr. Piatetsky-Shapiro is the founder of the Knowledge Discovery in Database conference series (KDD, now the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). He organized and chaired the first three KDD workshops in 1989, 1991, and 1993, and was again the General Chair of the KDD-98 conference. He helped create ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining.

He is the Editor and Publisher of KDnuggets News (the leading newsletter on data mining and knowledge discovery) and the associated www.KDnuggets.com site, the leading site for data mining and knowledge discovery.

He also served on the program committees of numerous other conferences and workshops in artificial intelligence and databases, and was the founding editor of the Data Mining and Knowledge Discovery journal.

Dr. Piatetsky-Shapiro has over 60 publications, including two best-selling books and several edited collections on topics related to data mining and knowledge discovery.

2002 Service Award: Dr. Ramasamy (Samy) Uthurusamy

The 2002 SIGKDD Service Award went to Dr. Sam Uthurusamy. Uthurusamy helped launch and sustain the KDD workshops and subsequent KDD conferences, which were the seeds that led to the formation of the KDD community and discipline.

Uthurusamy was a member of Organizing and Program Committees of the first KDD-89 workshop, and the subsequent KDD-91 and KDD-93 workshops. He was Program Co-Chair of the KDD-94 workshop. Dr. Uthurusamy then used his contacts with AAAI to transform the workshop into a conference. He was Program Co-Chair of the first KDD-95 conference, General Chair of the KDD-97 conference, and Sponsorship Chair of the KDD-98 and KDD-99 conferences. He is currently serving as a Director of ACM SIGKDD.

Dr. Uthurusamy evangelized the importance of KDD in many forums. He was a Co-Editor of the book "Advances in Knowledge Discovery and Data Mining" published by MIT/AAAI Press in 1996. He was also Co-Editor of the November 1996 "Communications of ACM" Special Issue on Knowledge Discovery and Data Mining. He is Co-Editor of the follow-up CACM Special Issue on KDD to be published in August 2002. He is a founding editorial member of the KDD Journal. He has presented numerous keynote and invited talks across the world (USA, Germany, Australia, India, etc.) on KDD, and organized and participated in many panels. In addition, he got many KDD people invited to give talks at related conferences.

Dr. Uthurusamy has also been very active in the AI community. He was Technical Program Co-Chair of IAAI-99, IAAI-98, 2000 Indian AI Conference on Knowledge Based Computer Systems (KBCS-00), 1998 Indian AI Conference on Knowledge-Based Computer Systems (KBCS-98), and the SPIE 1993 AI Conference. He is on the Advisory Board of the 2001 International Joint Conferences on AI (IJCAI-01).

Dr. Uthurusamy is currently General Director of Emerging Technologies, Information Systems and Services Division of General Motors Corporation. In this role, he evangelized and applied KDD technology in General Motors, and received the Charles McCuen Special Achievement Award from General Motors R&D Center for his work on GM-specific KDD applications.

2003 Service Award: Dr. Usama Fayyad

Dr. Fayyad is one of the pioneers in data mining research and one of the initiators of the KDD community as we know it today. In addition to his excellent research and development achievements in data mining and machine learning, he has actively served the KDD community since the days of the first KDD workshop at IJCAI-1989, and he plays a leading role in KDD community ever since.

Dr. Fayyad served as the program co-chair of KDD-94 workshop. He co-lead the effort to transition the workshops to International conference status and co-chaired the First International Conference on Knowledge Discovery and Data Mining in 1995. He later served as the general chair of KDD-96 and KDD-99 conferences.

In 1998 he was instrumental in creating ACM SIGKDD, ACM Special Interest Group on Knowledge Discovery and Data Mining, and was later elected to the board of Directors of SIGKDD.

He co-founded and is currently the Editor-in-Chief of the scientific technical journal: Data Mining and Knowledge Discovery, which is the major journal in data mining. He also co-founded and serves as Editor-in-Chief of SIGKDD Explorations, the newsletter of SIGKDD. He serves on several Editorial Boards including the Communications of the ACM and Artificial Intelligence Magazine.

By these services, he has contributed significantly and distinctly to the formation and the healthy growth of KDD community.

Dr. Fayyad was one of the editors of book "Advances in Knowledge Discovery and Data Mining", which served for many data miners as a first introduction or a text book to the field. This book has been and is a major reference for many researchers and scholars. In addition, Dr. Fayyad also co-edited a leading book on information visualization in data mining.

Finally, Dr. Fayyad has been very influential in the success of the poster sessions at the KDD conference. It is becoming a tradition to allow poster authors to present their work in a two minutes quick presentation at the conference. Usama has been exceptionally good at chairing the poster sessions and making it fun to attend. It is so successful that it has been copied at many other conferences.

2004 Service Award: Dr. Xindong Wu

The 2004 SIGKDD service award went to Xindong Wu, Professor and Chairman of the Computer Science Department at the University of Vermont. He was the Founding Chair (April 1998 - April 2001) of the Steering Committee for the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), which has become the leading Asian data mining conference. He served as a program chair of PAKDD 98.

In 1999, he started the Knowledge and Information System (KAIS) journal and served as the Executive Editor since then. KAIS has become one of the main journals for publishing data mining related research .

In 2001 he founded the IEEE International Conference on Data Mining (ICDM) which has quickly become one of the premier conferences in the data mining field, receiving about 500 submissions in 2003-2004. He currently serves as the Chair of the Steering Committee for the IEEE International Conference on Data Mining (ICDM) and Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI). He has been working tirelessly to promote the data mining field in the IEEE community and in the world.

In addition to his significant service contributions, Dr. Xindong Wu is also very active and productive in data mining research and has an excellent track record of publications including eight books (author of two, co-author of one and co-ed of five) and numerous journal and conference papers in data mining.

2005 Service Award: the Weka team

For their development of the freely-available Weka Data Mining Software, including the accompanying book Data Mining: Practical Machine Learning Tools and Techniques (now in second edition) and much other documentation.

The Weka team includes Ian H. Witten and Eibe Frank, and the following major contributors (in alphabetical order of last names): Remco R. Bouckaert, John G. Cleary, Sally Jo Cunningham, Andrew Donkin, Dale Fletcher, Steve Garner, Mark A. Hall, Geoffrey Holmes, Matt Humphrey, Lyn Hunt, Stuart Inglis, Ashraf M. Kibriya, Richard Kirkby, Brent Martin, Bob McQueen, Craig G. Nevill-Manning, Bernhard Pfahringer, Peter Reutemann, Gabi Schmidberger, Lloyd A. Smith, Tony C. Smith, Kai Ming Ting, Leonard E. Trigg, Yong Wang, Malcolm Ware, and Xin Xu.

The Weka team has put a tremendous amount of effort into continuously developing and maintaining the system since 1994. The development of Weka was funded by a grant from the New Zealand Government's Foundation for Research, Science and Technology.

The key features responsible for Weka's success are:

• it provides many different algorithms for data mining and machine learning
• is is open source and freely available
• it is platform-independent
• it is easily useable by people who are not data mining specialists
• it provides flexible facilities for scripting experiments
• it has kept up-to-date, with new algorithms being added as they appear in the research literature.

The Weka Data Mining Software has been downloaded 200,000 times since it was put on SourceForge in April 2000, and is currently downloaded at a rate of 10,000/month. The Weka mailing list has over 1100 subscribers in 50 countries, including subscribers from many major companies.

There are 15 well-documented substantial projects that incorporate, wrap or extend Weka, and no doubt many more that have not been reported on Sourceforge.

Ian H. Witten and Eibe Frank also wrote a very popular book "Data Mining: Practical Machine Learning Tools and Techniques" (now in the second edition), that seamlessly integrates Weka system into teaching of data mining and machine learning. In addition, they provided excellent teaching material on the book website.

This book became one of the most popular textbooks for data mining and machine learning, and is very frequently cited in scientific publications.

Weka is a landmark system in the history of the data mining and machine learning research communities, because it is the only toolkit that has gained such widespread adoption and survived for an extended period of time (the first version of Weka was released 11 years ago). Other data mining and machine learning systems that have achieved this are individual systems, such as C4.5, not toolkits.

Since Weka is freely available for download and offers many powerful features (sometimes not found in commercial data mining software), it has become one of the most widely used data mining systems. Weka also became one of the favorite vehicles for data mining research and helped to advance it by making many powerful features available to all.

In sum, the Weka team has made an outstanding contribution to the data mining field.

2006 Service Award: Dr. Won Kim

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.

2007 Service Award: Dr. Robert Grossman

Robert Grossman is recognized for his key role in the development of open and scalable architectures and standards for the SIGKDD and Global KDD Communities. Grossman was one of the Founders of the Data Mining Group in 1998, which develops the Predictive Model Markup Language (PMML). He has been its Chair since it was started; and, during this time, it has released nine versions of PMML. PMML has seen wide spread adoption by the KDD community, in part, because:

• PMML supports the sharing of statistical and data mining models in a platform and application independent fashion.
• PMML supports architectures in which one application produces PMML models (called the PMML Producer) and another application, which may not even be a data mining application, consumes PMML models (called the PMML Consumer or scoring engine).
• PMML supports KDD service oriented architectures.
• PMML facilitates the storing of models in model repositories.
• PMML supports applications in which models must be audited for compliance and other regulatory requirements.

For the past 10 years, Grossman has led two international testbeds for high performance and distributed data mining, which have been used by over fifty different organizations and groups to test, benchmark, and develop innovative technology for high performance and distributed data mining and knowledge discovery. The testbeds have also been used to develop and benchmark grid and service oriented technologies for mining large remote and distributed data sets. The first testbed was called the Terabyte Challenge and operated from 1995 to 1999, when working with a terabyte of data was still relatively rare. The second tested called the Teraflow Testbed was started in 2004 and will operate until at least 2008. Today when most distributed data mining takes place at 1-100 Mbps, the Teraflow Testbed can be used to mine data at 1-10 Gbps over wide area high performance networks.

Grossman has a long history of serving the KDD community. He was the Industrial Track Co-Chair for KDD 2006, the General Chair of KDD 2005, the Sponsorship Chair for KDD 2000 and 2001, and the co-chair of the First and Second SIAM International Conferences on Data Mining (SDM-01 and SDM-02).

Grossman has published over 140 research and technical papers in international conferences and journals. In 2005, he led the team that won the first annual High Performance Analytics Challenge at the ACM/IEEE International Conference for High Performance Computing and Communications (SC 2005). He also led teams that won prizes involving high performance data mining and related areas at SC 2006, SC 1999, and SC 1998, SC 1996 and SC 1995.

Grossman is the Director of the National Center for Data Mining at the University of Illinois at Chicago and the Managing Partner of Open Data Group.