== Second Workshop on Architectures and Systems for Big Data (ASBD) ==         Co-located with ISCA-39, June 09, 2012, Portland, Oregon            https://sites.google.com/site/asbdworkshop/ ** DEADLINE EXTENDED to March 23 ** - Submissions deadline: March 23, 2012; - Author notification: April 18, 2012 Workshop Co-Organizers: - Jichuan Chang, HP Labs - Jian Li, IBM Research - Evan Speight, IBM Research Program Committee Chair: - Peter Hofstee, IBM Research Program Committee: - Chaitanya Baru, SDSC - Viraj N. Bhat, Yahoo! - Shimin Chen, HP Labs - Robert Hundt, Google - Christos Kozyrakis, Stanford University - Trevor Mudge, University of Michigan - Partha Ranganathan, HP Labs - Ben Sigelman, Google - Steve Swanson, UCSD - Dimitris Tsirogiannis, Microsoft Research - Chitra Venkatramani, IBM Research - Ren Wang, Intel Labs - Ben Yang, IBM Research - Lin Zhong, Rice University * Submission format: All papers should be submitted in PDF format, using 10 point or larger font for text (8 points or larger for figures and tables), total length not to exceed 6 pages. * Submission website: http://www.easychair.org/conferences/?conf=asbd2012 (opens Feb 15, 2012) The term "Big Data" refers to the continuing massive expansion in the data volume and diversity as well as the speed and complexity of data processing. The use of big data underpins critical activities in all sectors of our society. Achieving the full transformative potential of big data in this increasingly digital world requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive-scale analytics. We are pleased to request papers for presentation at the upcoming Second Workshop on Architectures and Systems for Big Data (ASBD 2012) held in conjunction with ISCA-39. The workshop will provide a forum to exchange research ideas related to all critical aspects of emerging analytics systems for big data, including architectural support, benchmarks and metrics, data management software, operating systems, and emerging challenges and opportunities. We hope to attract a group of inter- disciplinary researchers from academia, industry and government research labs. To encourage discussion between participants, the workshop will include significant time for interactions between the presenters and the audience. We also plan to have a keynote speaker and/or panel session. Topics of interest include but are not limited to: * Processor, memory, and system architectures for data analysis * Benchmarks, metrics, and workload characterization * Debugging and performance analysis tools for analytics and big data * Accelerators for analytics and data-intensive computing * Implications of data analytics to mobile and embedded systems * Energy efficiency and energy-efficient designs for analytics * Availability, fault tolerance and recovery issues * Systems/networks for high concurrency or high bandwidth data streaming * Data management and analytics for vast amounts of unstructured data * Evaluation tools, methodologies and workload synthesis * OS, distributed systems and system management support * MapReduce and other processing paradigms for analytics We encourage both industry and academic researchers to submit their research. Preliminary results of interesting ideas and work-in-progress are welcome. Submissions that are likely to generate vigorous discussion will be favored!