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 second 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-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. Please submit papers to https://cmt2.research.microsoft.com/CLOUDB2010/

• Workshop Papers Due: July 19, 2010
• Notification of Acceptance: July 30, 2010
• Camera Ready: August 15, 2010 (hard deadline for publication)

General Co-Chairs:

Prof. Xiaofeng Meng, Renmin University of China, China
Dr. Ying Chen, IBM China Research Lab, China

PC Members:（TBD）

Accepted papers list:

• [2] Towards a Data-centric View of Cloud Security [Zhou, Wenchao]
• [4] ESQP: An Efficient SQL Query Processing for Cloud Data Management [Zhao, Jing]
• [7] Contract-based Cloud Architecture [Alnemr, Rehab]
• [10] Adaptive Query Execution for Data Management in the Cloud [Popescu, Adrian Daniel]
• [12] Dynamic Data Replication through Virtualization [Daudjee, Khuzaima]
• [5] Benchmarking Cloud-based Data Management Systems [Shi, Yingjie]
• [6] Towards Bipartite Graph Data Management [Zhao, Bin]
• [9] Comparing SQL and MapReduce to compute Naive Bayes in a Single Table Scan [Ordonez, Carlos]