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.

This is the third 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 privacy and security
• Cloud data management of sensor stream data
• Mobile cloud data management
• Cloud-based system designs including architecture, scalability, economy, consistence-availability-partition (CAP), and security
• Services discovery and content distribution in cloud computing infrastructures
• Cross-platform interoperability
• Query processing and indexing in cloud computing systems
• Access control in cloud computing systems
• 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

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.

To submit a paper, go to the CloudDB2011 Submission System. Only papers submitted via the CloudDB2011 Submission System will be considered for review.

• Individual Workshop Papers Due: June 29, 2011 Extended to: July 6th, 2011
• Notification of Acceptance: July 29, 2011
• Camera Ready: August 12, 2011

General Chairs:

Xiaofeng Meng, Renmin University of China

PC Members:

Lei Chen, The Hong Kong University of Science and Technology
Hai Jin, Huazhong University of Science and Technology
Kian-Lee Tan, National University of Singapore
Chunqiang Tang, IBM T. J. Watson Research
Mohamed Sharaf, University of Queensland
Maurizio Lenzerini, University of Rome La Sapienza
Wang-Chien Lee, Pennsylvania State University
Karu Sankaralingam, University of Wisconsin-Madison
Marcos K. Aguilera, Microsoft Research
Rajkumar Buyya, The University of Melbourne
Qiu Jie, IBM China Research Lab
Anna Liu, University of New South Wales
Sherif Sakr, National Information and Communications Technology Australia
Bin He, IBM Almaden Research Center

• Learning-based Entity Resolution with MapReduce [Lars Kolb; Hanna K?pcke; Andreas Thor; Erhard Rahm]
• Incremental Recomputations in MapReduce[Thomas J?rg; Roya Parvizi; Hu Yong; Stefan Dessloch ]
• Efficient Data Distribution Strategy for Join Query Processing in the Cloud [Haiping Wang; Xiaofeng Meng; Yunpeng Chai ]
• Trustworthy Middleware Services in the Cloud [Imad Abbadi]
• Cooperative Database Caching Within Cloud Environments [Andrei Vancea; Guilherme Sperb Machado; Laurent D’orazio; Burkhard Stiller]
• TeleDatA: Data Mining, Social Network Analysis and Statistics Analysis System based on Cloud Computing in Telecommunication Industry [Yuxiao Dong; Qing Ke]
• Authentication of Range Query Results in MapReduce Environments [Ziwei Yang; Shen Gao; Jianliang Xu; Byron Choi]