We are pleased to announce that Professor Robert Grossman is the winner of the ACM SIGKDD 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.







































 
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