ACM SIGKDD is pleased to announce that Usama M. Fayyad is the winner of its 2007 Innovation Award. Fayyad is recognized for his seminal work on the development data mining, machine learning algorithms and their scalability to massive database systems, and fundamental applications of data mining in scientific discovery and commercial database systems.

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, Leo Breiman, and Ramakrishnan Srikant.

The award includes a plaque and a check for $2,500, to be presented at KDD-2007 (The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining) Opening Plenary Session on August 12, 2007 in San Jose, CA. Fayyad will present the Innovation Award Lecture immediately after the award presentations.

Fayyad's contributions span fundamental technical innovation and significant large-scale applications of the technology in science data analysis, commercial practice, and commercial database systems. His early contributions include the theoretical analysis of decision tree learning algorithms and the invention of some of the fundamental algorithms in decision tree induction from large scale data. His algorithm for discretization of numerical attributes has been and remains the state-of-the-art method in the machine learning and data mining communities for the past decade. His work on applications of data mining and statistical pattern recognition to massive scientific data sets in Astronomy, Planetary Geology, and remote sensing at NASA's Jet Propulsion Lab (JPL), California Institute of Technology have led to solving significant scientific advances and new discoveries in those fields. He received a U.S. Government Medal from NASA for this work as well as the JPL Lew Allen Award for Research Excellence from Caltech -- the highest honor granted to JPL scientists.

Fayyad's contributions to database systems involved inventing scalable data mining algorithms for massive databases, co-authoring new SQL Extensions and leading development work for embedding data mining algorithms inside the database engine of Microsoft's SQL Server 2000 system. The latest version of SQL Server 2006 still includes Fayyad's algorithms as well as derivatives and descendants of the core methodology he introduced.

Fayyad has played a leading innovative role in the development of the data mining industry by launching a startup company, Revenue Science Inc. (digiMine, Inc.) that developed an innovative business model around hosted on-demand applications of data mining, business intelligence, and targeting algorithms. His second start-up, DMX Group was acquired by Yahoo! Inc. in 2004 where, as a member of the senior executive team as the industry's first Chief Data Officer, he presides over the world's largest data streams (processing over 25 terabytes of data per day), and launching and overseeing Yahoo! Research which has the mission of inventing the new sciences underlying the data-rich areas of Internet, Microeconomics of the Web, and Search and Information Navigation over the world's largest collection of knowledge: the world-wide web.

Fayyad is co-editor of two influential books in data mining and knowledge discovery and has published over 100 technical articles in machine learning, Artificial intelligence, data mining and databases. He is a prolific inventor with over 30 patents issued and over 50 filed patents in the areas of data mining, on-line marketing and the Internet.

Fayyad has actively participated in the KDD community. He served as Program Co-Chair of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), and served as general chair of KDD-96 and as first general chair when the conference moved to ACM SIGKDD in 1999. He is the founding Editor-in-Chief of the primary technical journal in the field: Data Mining and Knowledge Discovery and remained as editor-in-chief for its first decade. He is founding Editor-in-Chief of ACM's SIGKDD Explorations, the official newsletter of the SIGKDD. He is a Fellow of the AAAI (Association for Advancement of Artificial Intelligence) and a Fellow of the ACM and is the recipient of many industry awards.

ACM SIGKDD is pleased to present Usama M. Fayyad its 2007 Innovation Award for his seminal contributions to machine learning and data mining algorithms that scale to large commercial database systems and for his fundamental applications in mining massive science data sets leading to significant new scientific discoveries.

ACM SIGKDD is pleased to announce that Robert Grossman is the winner of its 2007 Service Award. 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.

The 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 disseminating technical information to the field, leading organizations or projects that contribute technically to the field as a whole, furthering KDD education, or increasing funding to the KDD community.

The previous SIGKDD Service Award winners were Gregory Piatetsky-Shapiro, Ramasamy Uthurusamy, Usama M. Fayyad, Xindong Wu, the Weka team lead by Ian Witten and Eibe Frank, and Won Kim.

The award includes a plaque and a check for $2,500, to be presented at KDD-2007 (The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining) Opening Plenary Session on August 12, 2007 in San Jose, CA.

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.

ACM SIGKDD is pleased to present Grossman its 2007 Service Award for his significant service and contributions to the global KDD community.

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 and Eibe Frank (University of Waikato, New Zealand)
Xindong Wu (University of Vermont, USA)