HPDM 2006: 9th International Workshop on
in conjunction with
High Performance and Distributed Mining
April 22, 2006
This is the 9th workshop on this theme held annually.
Traditionally, the workshop has been held along-side the
SIAM datamining (SDM) conference, even if the first four editions were
organized in conjunction with IPDPS, and were held at Orlando (HPDM'98),
San Juan (HPDM'99), Cancun
and San Francisco (PDDM'01).
Over the last three years the workshop has had invited papers in the
areas of mobile and location-aware data mining issues (HPDM:RLM'02),
pervasive and stream datamining
grid data mining ( HPDM:GRID'04).
Last year, the workshop again invited papers on all aspects of high performance data mining
- Paper Submissions: January 9, 2006
- Notification: Feb 1, 2006
- Camera ready: February 14, 2006
Over the years the definition of high performance computing has taken on various forms as a function of the types of technical and creative uses and the underlying semantics of the applications driving them. Traditional definitions often refer to the problem of using high end parallel computers to meet the need of scientific applications. However, high performance computing can also include the need for fast sequential algorithms that target memory and I/O performance. The last decade has seen the growth and importance of grid computing where resources and data are physically distributed. This has led to the development of high performance distributed algorithms over the computational grid. A wide spectrum of data mining communities has participated in high performance data mining. To maximize synergic effect, this year's workshop will be particularly focusing on leveraging interactions and collaboration between different communities. We welcome papers on all aspects of high performance data mining. Topics of interest include but are not limited to:
Grid-based data mining algorithms and systems
Distributed techniques for incremental, exploratory and interactive mining.
Distributed techniques for security, privacy preserving data mining.
Peer-to-Peer Data Mining
High performance data stream mining and management.
Resource and location-aware mining algorithms.
Architecture-aware data mining algorithm.
Data mining in mobile environments.
Theoretical foundations for resource-aware mining in a mobile, streaming and/or distributed environment.
Systems support for resource and location aware data mining.
Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining and pre-procesing and post-processing tasks.
Parallel or distributed frameworks for stream management,
KDD systems, and parallel or distributed mining.
Applications of parallel and distributed data mining (PDDM) in business, science, engineering, medicine, and other disciplines.
We invite papers treating the above topics in one of many ways. The papers could describe new results, give overview or experiences with existing systems, describe new and emerging applications, present work in progress where interesting insights have been gained, or critically survey existing work. The papers should not exceed 3000 words. You can submit by emailing the PS or PDF file to email@example.com.
- Gagan Agrawal, Ohio State University, USA
- Ian Davidson, University at Albany, USa
- Sara Graves, University of Alabama, Huntsville, USA
- Ruoming Jin, Kent State University, USA
- Hillol Kargupta, University of Maryland, Baltimore County, USA
- Shonali Krishnaswamy, Monash University, Australia
- Vipin Kumar, University of Minnesota, USA
- Salvatore Orlando, University of Venice, Italy
- Raffaele Perego, CNR, Italy
- Krishnamoorthy Sivakumar, Washington State University, USA
- Domenico Talia, University of Calabria, Italy
- Pang-Ning Tan, Michigan State University, USA
- Mohammed Zaki, RPI, USA
- Hillol Kargupta, University of Maryland, Baltimore County
- Vipin Kumar, University of Minnesota
- Srinivasan Parthasarathy (chair), Ohio State University
- David Skillicorn, Queens University
- Mohammed Zaki, RPI