Workshop on Data analytics in the Cloud

Due to unprecedented data growth, a need for rich data analysis on petabyte-scale data is emerging. To support such analysis, new data-centric programming paradigms and data management systems are being established, popularized by Google's MapReduce framework and its open-source implementation, Hadoop. The new data analysis market raises several research challenges spanning the whole system stack, from storage and network technologies to programming languages.

At the same time, cloud computing is emerging as a cost-effective paradigm for massively scalable, fault-tolerant, and adaptive computation. Cloud computing architectures scale to massive numbers of commodity computers and adapt to changing hardware availability and requirements by dynamically allocating virtualized computing nodes. Cloud computing systems often use a computational model motivated by functional programming, abstracting away the internals of computation. Both enterprise and client data are moving to the cloud for reasons of cost, reliability, and manageability. This migration poses significant challenges on current systems. The economies of scale provided by cloud computing provide opportunities for richer data analysis on even larger data sets. The new data analysis applications and the unprecedented scale is not adequately served by current offerings, including commercial DBMSs and analytics systems, and open-source cloud computing systems.

This workshop will provide a perfect forum to bring together researchers and practitioners interested in big data analytics, cloud computing, and their intersection. The workshop will help to foster future collaborations and the formation of a community that sets the ground of this emerging field.

Topics of Interest

Submissions of original research contributions are invited for all relevant topics, including, but not limited to:

Analytic frameworks for cloud systems
Data models and query languages
Parallel query processing and optimization
Scalable storage and indexing
Workload management
Data privacy and security
Administration and manageability
Benchmarking, tuning, and testing
Energy management
Industrial experience and use cases
Data science and analytics technologies
Scientific data management
Scalable machine learning

Important Dates

Submission deadline (extended): December 14, 2011
Notification to authors: January 15, 2012
Camera ready papers due: February 2, 2012
Workshop: March 30, 2012

Submission Guidelines

All papers should be formatted using the double-column ACM format (templates available at: http://www.acm.org/sigs/publications/proceedings-templates). The workshop solicits:

Regular Research Papers (maximum length: 12 pages)
Vision Papers (maximum length: 6 pages)
Experience Reports (maximum length: 6 pages)

Papers should be submitted using the conference management system. The workshop proceedings will be published in the ACM Digital Library.

Submission site: https://cmt.research.microsoft.com/DANAC2012/

People

PC chairs

Tim Kraska, UC Berkeley, USA
Kostas Tzoumas, TU Berlin, Germany

Steering Committee

Michael J. Carey, UC Irvine, USA
Volker Markl, TU Berlin, Germany

Program Committee

Shivnath Babu, Duke University, USA
Magdalena Balazinska, University of Washington, USA
Alexandru Iosup, TU Delft, The Netherlands
Donald Kossmann, ETH Zurich, Switzerland
Sam Madden, MIT, USA
Ioana Manolescu, INRIA, France
Jignesh Patel, University of Wisconsin-Madison, USA
Christopher Re, University of Wisconsin-Madison, USA
Mirek Riedewald, Northeastern University, USA
Marcos Vaz Salles, University of Copenhagen, Denmark
Florian Waas, EMC/Greenplum, USA

Keynotes

Keynote 1: Roger Barga, Microsoft Research, USA

Keynote 2: Florian Waas, EMC, USA