1. Description
Technology advances in communications, computation, and storage result in huge collections of data, capturing information of value to business, science, government, and society. Data volumes are currently growing faster than Moore’s law. Looking forward, the exponential growth is not likely to stop. The huge size of data is imposing big challenges on infrastructure for data storage which can achieve economical scaling to even more than Petabyte, massively parallel query execution, and facilities for analytical processing. Meanwhile, the rise of large data centers and cluster computers has created a new business model, cloud-based computing, where businesses and individuals can rent storage and computing capacity, rather than making the large capital investments needed to construct and provision large-scale computer installations. Cloud-based data storage and management is a rapidly expanding business. Whilst these emerging services have reduced the cost of data storage and delivery by several orders of magnitude, there is significant complexity involved in ensuring large data service can scale when needed to ensure consistent and reliable operation under peak loads. Cloud-based environment has the technical requirement to manage data center virtualization, lowers cost and boosts reliability by consolidating systems on the cloud. In addition, in an ideal world, the cloud systems should be geographically dispersed to reduce their vulnerability due to earthquakes and other catastrophes, which increase technical challenge on a great level of distributed data interoperability and mobility.
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2. Scope and novelty of the workshop

This is the first workshop in CIKM conference that addresses the challenge of large data management based on cloud computing infrastructure. This workshop will bring together researchers and practitioners in cloud computing and data-intensive system design, programming, parallel algorithms, data management, scientific applications, and information-based applications to maximize performance, minimize cost and improve the scale of their endeavors.

This workshop welcomes papers that address fundamental research issues in this challenging area, with emphasis on personal and social applications of cloud-based data management. We also encourage papers to report on system level research related to cloud computing and data-intensive computing. A number of invited papers will also be solicited.

Topics of interest include, but are not limited to

  • cloud computing infrastructure for big data storage and computing;
  • cloud-based big data system, including architecture, scalability, economy, consistence-availability-partition (CAP), and security;
  • services and data discovery and content and service distribution in cloud computing infrastructures;
  • cross-platform interoperability;
  • security and risk in the cloud/Security and risk in the big data management
  • service-level agreements, business models, and pricing policies;
  • novel data-intensive computing applications
  • language for massively parallel query execution
  • data intensive scalable computing
  • content distribution systems for big data
  • data management within and across data centers
  • large scale analytical methodology and algorithm
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3. Submission

Manuscripts should be formatted using the ACM camera-ready templates (both for MS word and Latex) available at http://www.acm.org/sigs/pubs/proceed/template.html. There are two styles on the website. Both the Strict Adherence to SIGS and the Tighter Alternate style are allowed. Papers cannot exceed 8 pages in length. Please submit papers to clouddb09@gmail.com.

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4. Important Dates

  • Submission deadline: July 5th 2009
  • Notification date: Aug 11st 2009
  • Camera-ready submission deadline: Aug 20th 2009
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    5. Organizing Committee

    Workshop Co-Chairs:

    Prof. Xiaofeng Meng, Renmin University of China, China
    Dr. Haixun Wang, IBM T. J. Watson Research, USA
    Dr. Ying Chen, IBM China Research Lab, China


    Local Arrangement Co-Chairs:

    Dr. Jiaheng Lu, Renmin University of China, China
    Dr. Jie Qiu, IBM China Research Lab, China


    PC members (More to be added):

    • Lei Chen, Hong Kong University of Science and Technology, Hong Kong
    • Jidong Chen, EMC China Lab, China
    • Brian Frank Cooper, Yahoo Research, U.S.A
    • Hai Jin, Huazhong University of Science and Technology, China    
    • Avinash Lakshman, Facebook, U.S.A
    • Chen Li, UCI, U.S.A
    • Xiaoming Li, Peking University, China     
    • Zhanhuai Li, Northwestern Polytechnic University, China
    • Jian Pei, Simon Fraser University, Canada
    • Kian-Lee Tan, National University of Singapore, Singapore    
    • Changjie Tang, Sichuan University, China
    • Yufei Tao, Chinese University of Hong Kong, Hong Kong
    • Shivakumar Vaithyanathan, IBM T. J. Watson Research, U.S.A
    • Kyu-Young Whang , KAIST, Korea
    • Guoren Wang, Northeastern University China, China
    • Jianliang Xu, Hong Kong Baptist University, Hong Kong
    • Aoying Zhou, Fudan University, China    
    • Lizhu Zhou, Tsinghua University, China

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