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CloudDB 2009 : The First International Workshop on Cloud Data Management (CloudDB2009)

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Conference Series : Cloud Data Management
 
Link: http://www.clouddb.org/CloudDB09/
 
When Nov 6, 2009 - Nov 6, 2009
Where HongKong, China
Submission Deadline Jul 5, 2009
Categories    databases   cloud
 

Call For Papers

CloudDB’09 Call for Papers
First International Workshop on Cloud Data Management

In conjunction with the ACM 18th Conference on Information and Knowledge Management



November 6, 2009, Hong Kong, China

http://www.clouddb.org/



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. While 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.



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

l cloud computing infrastructure for big data storage and computing;

l cloud-based big data system, including architecture, scalability, economy, consistence-availability-partition (CAP), and security;

l services and data discovery and content and service distribution in cloud computing infrastructures;

l cross-platform interoperability;

l security and risk in the cloud/Security and risk in the big data management

l service-level agreements, business models, and pricing policies;

l novel data-intensive computing applications

l language for massively parallel query execution

l data intensive scalable computing

l content distribution systems for big data

l data management within and across data centers

l large scale analytical methodology and algorithm



Submission Papers

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. Selected papers will be included in JCST Special Issue on Trends Changing Data Management. Some selected papers will be encouraged for submission to TKDE special issue on 'cloud computing' for consideration of publication; the papers will have to meet TKDE publication requirements and will go through the normal reviewing process.



Important Dates

Submission deadline: July 5th 2009

Notification date: Aug 11st 2009

Camera-ready submission deadline: Aug 20th 2009



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:

• Lei Chen, HKUST, Hong Kong

• Jidong Chen, EMC Research China, China

• Brian Frank Cooper, Yahoo! Research, U.S.A

• Hai Jin, Huazhong Univ. of Sci. and Tech., China

• Avinash Lakshman, Facebook, U.S.A

• Chen Li, UCI, U.S.A

• Xiaoming Li, Peking University, China

• Ying Li, IBM Research, China

• Zhanhuai Li, Northwestern Polytechnic Univ., China• Xuemin Lin, Univ. of New South Wales, Australia

• David Lomet, Microsoft, U.S.A

• Zaiqing Nie, Microsoft Research Asia, China

• Jian Pei, Simon Fraser University, Canada

• Adam Silberstein, Yahoo! Research, U.S.A

• Kian-Lee Tan, NUS, Singapore

• Changjie Tang, Sichuan University, China

• Chunqiang Tang, IBM T. J. Watson Research, U.S.A

• Yufei Tao, CUHK, Hong Kong

• Shivakumar Vaithyanathan, IBM Research, U.S.A

• Kyu-Young Whang, KAIST, Korea

• Guoren Wang, Northeastern University, China

• Ji-Rong Wen, Microsoft Research Asia, China

• Jianliang Xu, HKBU, Hong Kong

• Jiangming Yang, Microsoft Research Asia, China

• Jun Yang, Duke University, U.S.A

• Sai Zeng, IBM T. J. Watson Research, U.S.A

• Rui Zhang, University of Melbourne, Australia

• Aoying Zhou, Fudan University, China

• Lizhu Zhou, Tsinghua University, China

• Xiaodong Zhou, AOL China Lab, China

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