KDCloud 2013 - The 4th International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms (KDCloud, 2013)

The 4th International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms (KDCloud, 2013) provides an international platform to share and discuss recent research results in adopting cloud and distributed computing resources for data mining and knowledge discovery tasks.
Synopsis: Processing large datasets using dedicated supercomputers alone is not an economical solution. Recent trends show that distributed computing is becoming a more practical and economical solution for many organizations. Cloud computing, which is a large-scale distributed computing, has attracted significant attention of both industry and academia in recent years. Cloud computing is fast becoming a cheaper alternative to costly centralized systems. Many recent studies have shown the utility of cloud computing in data mining, machine learning and knowledge discovery. This workshop intends to bring together researchers, developers, and practitioners from academia, government, and industry to discuss new and emerging trends in cloud computing technologies, programming models, and software services and outline the data mining and knowledge discovery approaches that can efficiently exploit this modern computing infrastructures. This workshop also seeks to identify the greatest challenges in embracing cloud computing infrastructure for scaling algorithms to petabyte sized datasets. Thus, we invite all researchers, developers, and users to participate in this event and share, contribute, and discuss the emerging challenges in developing data mining and knowledge discovery solutions and frameworks around cloud and distributed computing platforms.
Topics: The major topics of interest to the workshop include but are not limited to:
Programing models and tools needed for data mining, machine learning, and knowledge discovery
Scalability and complexity issues
Security and privacy issues relevant to KD community
Best use cases: are there a class of algorithms that best suit to cloud and distributed computing platforms
Performance studies comparing clouds, grids, and clusters
Performance studies comparing various distributed file systems for data intensive applications
Customizations and extensions of existing software infrastructures such as Hadoop for streaming, spatial, and spatiotemporal data mining
Applications: Earth science, climate, energy, business, text, web and performance logs, medical, biology, image and video.
Proceedings: Accepted papers will be included in a ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press, which will also be included in the IEEE Digital Library.
Paper Submission: This is an open call-for-papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 6 pages) describing work-in-progress or case studies. Only original and high-quality papers conforming to the ICDM 2013 standard guidelines will be considered for this workshop.
Program Chairs
Ranga Raju Vatsavai, Oak Ridge National Laboratory, USA
Sanjay Ranka, University of Florida, USA
Latifur Khan, University of Texas, Dallas, USA
Government, Industry, and Publicity Chairs
Varun Chandola, Oak Ridge National Laboratory, USA
Steering Committee
Vipin Kumar, University of Minnesota, USA
Program Committee (Under Construction)
Gagan Agrawal, Ohio State University
David E. Bernholdt, ORNL
Kanishka Bhaduri, NASA
Christian Böhm, Ludwig Maximilians Universität München
Peter A. Dinda , Northwestern University
Chris Dyer, CMU
Amol Ghoting, IBM T. J. Watson Research
Rob Gillen, ORNL
Rajeev Gupta, IBM India Research Lab
James Horey, ORNL
Shonali Krishnaswamy, Monash University
Kun Liu, Yahoo! Labs
Yan Liu, IBM TJ Watson
Michael May, Fraunhofer Institute for Autonomous Intelligent Systems, Germany
Ullas Nambiar, IBM India Research Lab
Byung Park, ORNL
Claudia Plant, Ludwig-Maximilians-Universität München
Ioan Raicu, Illinois Institute of Technology (IIT)
Lavanya Ramakrishnan, Lawrence Berkeley National Laboratory
Huzefa Rangawala, GMU
Louiqa Raschid, University of Maryland
Prasan Roy, IBM India Research Lab
Michal Shmueli-Scheuer, IBM Haifa Research Lab
Jie Tang, Tsinghua University
Sudharshan Vazhkudai, ORNL
Fei Wang, Cornell University
Mike Wilde, University of Chicago
Rong Yan, Facebook
Pusheng Zhang, Microsoft