Date: Jun 21, 2010
ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field.
Professor Faloutsos seminal cross-disciplinary works on power-law graphs, fractal-based analysis, time series, multimedia and spatial indexing are a rare combination of both impressive breadth as well as fundamental depth that set new research directions and inspired subsequent research impacting the KDD field.
His fundamental contributions to spatial and multimedia mining and indexing were well recognized. In 1997, VLDB recognized his R+tree method with its 'ten year paper' award. His work on Hilbert curves and fractals allowed for better access methods, as well as for modeling and selectivity estimations of real clouds of points. His work on QBIC (Query By Image Content) has been cited more than 1,000 times and inspired similar features that are in commercial products such as DB2 Image Extender, and the proposed GeMINI approach (GEneralized Multimedia INdexIng) became the norm in the field.
His expertise in the field of time series analysis and mining is widely recognized. His FODO 1993 paper introduced methods for efficient similarity search in sequence databases, and it has been cited over 1200 times. His SIGMOD 2004 paper on fast subsequence matching in time-series databases was recognized with the best paper award and has been cited over 1100 times. These seminal papers laid the foundations that inspired a new research area on time series databases.
His SIGCOMM 1999 paper discovered the fundamental power-laws in the Internet topology. This work pioneered the field, and has inspired many follow-up studies. As an indicator of the huge impact, the paper is the 5th most cited paper in 1999, it has been cited over 3000 times since, and will be presented with the "Test of Time" award at SIGCOMM 2010.
His KDD 2004
paper was a pioneering contribution to large-scale
graph mining. It introduced fast methods to discover connectivity
subgraphs, leveraging the duality between random-walk and
electricity-based similarity to achieve efficient methods that can be
applied at web scale. Going beyond static network models, his
seminal paper on modeling network evolution received the best
paper award in
This paper reveals surprising
phenomena in time-evolving networks such as shrinking diameter
and provides a mathematical model, the densification law, to
accurately explain those phenomena. His work has also introduced
fast methods for estimation of key graph metrics, winning two
consecutive SDM best paper awards in
his students won dissertation award and runner-up on topics related
to graph mining in
2008 and 2007, respectively.
His sustained contributions to KDD, with more than 200 highly cited publications, have been well recognized by a series of prestigious awards, including the ICDM Research Contributions Award in 2006 and 15 Best Paper awards from various highly competitive academic forums including KDD, ICDM, SDM, PKDD, PAKDD, SIGMOD, and VLDB.
His work has led not only to important publications, but also to several projects with great broader societal impact. For example, his NetProbe project, which developed tools combating Internet auction fraud, was widely reported by many major news media.
He served on the Board of Directors of the first ACM SIGKDD Executive Committee. He was Program Chair of KDD 2003 and SIGMOD 1999. He was an associate Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (TKDE), and is an associate editor of ACM Transactions on Knowledge Discovery from Data (TKDD). He has made significant contribution to the KDD community by continuously training many prominent students and young researchers.
He received his PhD in 1987 from the University of Toronto, Canada. Since 2000, he has been a full professor at the School of Computer Science at Carnegie Mellon University . He has been issued 5 patents with 4 more pending. He has delivered more than 100 Distinguished, Keynote, and Invited lectures.
The previous SIGKDD Innovation Award winners were Rakesh Agrawal, Jerome Friedman, Heikki Mannila, Jiawei Han, Leo Breiman, Ramakrishnan Srikant, Usama M. Fayyad, Raghu Ramakrishnan, and Padhraic Smyth.
The award includes a plaque and a check for $2,500 and will be presented at the Opening Plenary Session of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, on July 25, 2010 in Washington , DC .
Prof. Faloutsos will present the Innovation Award Lecture immediately after the awards presentations.
ACM SIGKDD is pleased to present Prof. Christos Faloutsos its 2010 Innovation Award for his foundational technical contributions to the KDD field.
2010 ACM SIGKDD Awards Committee
- Ramasamy Uthurusamy, Chair
- Robert Grossman ( University of Illinois at Chicago )
- Jiawei Han ( University of Illinois at Urbana-Champaign)
- Tom Mitchell ( Carnegie Mellon University )
- Gregory Piatetsky-Shapiro (KDnuggets)
- Raghu Ramakrishnan (Yahoo! Research)
- Sunita Sarawagi (Indian Institute of Technology , Bombay )
- Padhraic Smyth ( University of California at Irvine )
- Ramakrishnan Srikant (Google Research)
- Xindong Wu ( University of Vermont )
- Mohammed J. Zaki (Rensselaer Polytechnic Institute)
ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems. The award includes a plaque and a check for $2,500 and will be presented at the Opening Ceremony of the annual ACM SIGKDD International Conference on KDD. The Innovation Award recipient presents the Innovation Award Lecture immediately after the awards presentations.
About ACM SIGKDD
ACM SIGKDD, ACM's Special Interest Group on Knowledge Discovery and Data Mining (KDD), is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining. It established the Innovation and Service Awards to recognize outstanding technical and service contributions to the KDD field.
The Association for Computing Machinery (ACM) is the world's largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.