Best Research Paper Awards
Winner: FastANOVA: an efficient algorithm for genome-wide association study.
X. Zhang, F. Zou, W. Wang.
Best Application Paper Awards
Winner: Context-Aware Query Suggestion by Mining Click-Through and Session Data.
H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, H. Li.
Best Student Paper Awards
Winner:Effective Label Acquisition for Collective Classification. M. Bilgic,
SIGKDD to Honor Outstanding Doctoral Dissertations
This year's upcoming SIGKDD International Conference on Knowledge Discover and Data Mining, to be held in Las Vegas, August 24-27, will highlight the first annual ACM SIGKDD Doctoral Dissertation Award which will recognize excellent research by doctoral candidates in the field of data mining and knowledge discovery.
The SIGKDD Dissertation Award Committee is pleased to announce Dr. Xiaoxin Yin's dissertation titled "Scalable Mining and Link Analysis Across Multiple Database Relations" (advisor Jiawei Han, U. of Illinois at Urbana-Champaign) as the winner of this year's award. Dr. Yin's dissertation was selected from among a number of very strong candidates and receiving this award will serve as a clear recognition of its contributions to the KDD community. In addition to the winner, SIGKDD will recognize the runner up dissertation by Dr. Jimeng Sun, titled "Incremental Pattern Discovery on Streams, Graphs and Tensors" (advisor Christos Faloutsos, CMU).
Both the winner and the runner up will be recognized at the opening ceremonies if the conference with a plaque and will have an opportunity to present a summary of their dissertations during one of the regular research sessions. In addition, the winner will receive $2,500 honorarium and free registration to the conference.
This year, the Dissertation Award Committee has also chosen to recognize two additional finalists from among the candidates because of the excellent contributions made by their dissertations. These finalists are Dr. David Martens (for "Building Acceptable Classification Models for Financial Engineering Applications") and Dr. Pradeep Ravikumar (for "Approximate inference, structure learning and feature estimation in Markov Random Fields") both of whom will be recognized at the opening ceremonies and will receive a certificate of recognition.
Additional Information about the SIGKDD Dissertation Awards including eligibility criteria and the list of Committee members can be found at the SIGKDD Web site.
Contacts: Dr. Bamshad Mobasher, Chair SIGKDD Doctoral Dissertation Award Committee
ACM SIGKDD 2008 Innovation Award
ACM SIGKDD is pleased to announce that Raghu Ramakrishnan is the winner of its 2008 Innovation Award. Ramakrishnan is recognized for his seminal research on techniques for scaling data mining algorithms to large datasets, and on mining ordered and streaming data.
The 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, Ramakrishnan Srikant, and Usama Fayyad.
The award includes a plaque and a check for $2,500, to be presented at KDD-2008 (The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining) Opening Plenary Session on August 24, 2008 in Las Vegas, NV. Ramakrishnan will present the Innovation Award Lecture immediately after the award presentations.
Ramakrishnan's contributions span foundational technical innovation on algorithmic and systems aspects of data mining. His work on scalable data mining algorithms started with BIRCH, the first truly scalable clustering algorithm. BIRCH introduced the groundbreaking idea of a cluster feature, a concise summary of a cluster, which was then used in many subsequent clustering algorithms as an integral component. Because of its novelty and importance, this is one of the highest cited data mining papers in the last decade. Ramakrishnan later extended this work into a clustering framework for arbitrary metric spaces. He also worked on scalable algorithms for decision tree construction that are still considered state-of-the-art today.
BIRCH is also the first true data stream mining algorithm: it constructs a clustering model in a single scan over the data with limited memory. Such algorithms for mining data streams have become a very important area of research in the data mining community over the last decade.
Further, Ramakrishnan developed a general framework for incrementally mining evolving data and created a framework for measuring change in data streams, again, visionary research topics that have generated much follow-up work since then. His work also introduced a new construct for analysis of ordered data, reflected in the inclusion of WINDOW functions in the SQL language.
Ramakrishnan’s work includes important contributions to data anonymization, and applying the multi-dimensional model from OLAP to develop a framework for exploratory data mining.
In addition to his academic research at the University of Wisconsin-Madison, Ramakrishnan has been active in applying data mining in industry. From 2000 to 2003, he was CTO and chairman of QUIQ, a company that developed technology for mass collaboration, a visionary concept that now with the arrival of Web 2.0 has gained widespread acceptance; the QUIQ-powered Ask Jeeves AnswerPoint question-answering portal was the forerunner of similar portals from Amazon, Linked-In and Yahoo!.
As Chief Scientist for Audience at Yahoo! he has led the research on content optimization, i.e., the task of algorithmically selecting the right content to display on a page when a user visits a web portal. This technology is already having a significant impact in practice. At Yahoo!, Ramakrishnan is also leading the research in cloud computing to develop a family of data hosting and analysis services, which, among other applications, will make it much easier to do data mining on the massive datasets seen at web-scale.
Ramakrishnan was Program Co-Chair of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD 2000), and served as an Editor-in-Chief of the primary technical journal in the field, Data Mining and Knowledge Discovery.
He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD, and on the Board of Trustees of the VLDB Endowment.
He is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He has received several awards, including the ACM SIGMOD Contributions Award, a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship in Science and Engineering, and an NSF Presidential Young Investigator Award.
ACM SIGKDD is pleased to present Raghu Ramakrishnan its 2008 Innovation Award for his foundational contributions to the field.
2008 ACM SIGKDD Awards Committee