[hpc-announce] CFP: 1st International Workshop on Collaborative methodologies to Accelerate Scientific Knowledge discovery in big data (CASK) 2014 @ IEEE BigData 2014

Wed Jul 23 16:23:34 CDT 2014

Call for Papers

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1st International Workshop on Collaborative methodologies to Accelerate
Scientific Knowledge discovery in big data (CASK) 2014

Oct 27-30, 2014, Washington DC, US

In conjunction with:
2014 IEEE International Conference on Big Data (IEEE BigData 2014)

         http://bigscientificdata.org/cask14/
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Introduction
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Big Data has become an increasingly important part of life, and has become a
common buzzword used to describe many aspects of data intensive computing. One
of the unique aspects that we see to this new age of science is in terms of new
methods to collaborate and share ideas, data, and services. Service Oriented
Architectures have become commonplace in Enterprise computing, but its role in
scientific data has been often underplayed. Commonly scientists will write
software for themselves, and occasionally share their programs with their
colleagues. As computing moves to new levels of performance, by using
accelerators and many cores, one must rethink how scientific codes are produced
and move to new frameworks which promote collaboration.

In this workshop we want researchers to discuss techniques, infrastructure,
science drivers, and new ways to promote this new way of computing for
scientific applications. We will want to address fundamental issues in
workflows on and off large-scale high performance systems, clouds, IPads, and
mobile devices. Our overall view is that we can accelerate the scientific
knowledge discovery process by embracing new technologies where researchers can
share codes, workflows, data, and ultimately knowledge. Topics of interest in
this workshop will span from methods to share all aspects of code, data, and
workflows.  We will investigate the topic of how do you share Big Data, when
data gets to extreme sizes.  What new services need to be developed in order to
promote Big Data for science and engineering aspects? How can we get
researchers across the globe to develop shareable code and participate in the
greater science community? Just like math was a common language that
researchers could share and everyone understand, what are the new pieces of
software which must be developed in order to ensure collaboration across the
end to end lifecycle of scientific data?  The workshop will provide a venue to
show what has worked across different communities, and how to bring
collaboration to new scientific communities.  The workshop will bring together
DOE and NSF researchers along with researchers in the enterprise to present
papers, along with give invited talks. We will also conclude with a panel with
many leading experts in the field. We will also feature one panel which will
include experts in scientific and enterprise Big Data.

Topics
Topics of interest include, but are not limited to:
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* Data at Rest (Storage)
* Data in Motion (Data Streaming)
* Storage Systems (Database, file systems)
* Resource Management
* Query and Search
* Acquisition, Integrating, Cleaning and Best Practice
* Privacy
* Provenance
* Algorithms
* Analytics
* Visualization
* Near Real-time Decision Making
* Data Fusion
* Workflows
* Programming Models (e.g. MapReduce, MPI, etc.)

Important Dates
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* Papers Due: September 1st, 2014
* Notification of Acceptance: September 20th, 2014
* Camera Ready Papers Due: October 5th, 2014

Paper Submission
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Authors are invited to submit papers electronically. Submitted manuscripts
should be structured as technical papers and may not exceed 6 letter size
(8.5 x 11) pages including figures, tables and references using the IEEE format
for conference proceedings (print area of 6-1/2 inches (16.51 cm) wide by 8-7/8
inches (22.51 cm) high, two-column format with columns 3-1/16 inches (7.85 cm)
wide with a 3/8 inch (0.81 cm) space between them, single-spaced 10-point Times
fully justified text). Submissions not conforming to these guidelines may be
returned without review. Authors should submit the manuscript in PDF format and
make sure that the file will print on a printer that uses letter size (8.5 x 11)
paper. The official language of the meeting is English. All manuscripts will be
reviewed and will be judged on correctness, originality, technical strength,
significance, quality of presentation, and interest and relevance to the
conference attendees. Papers conforming to the above guidelines can be
submitted through the CASK 2014 paper submission system
(https://cmt.research.microsoft.com/CASK2014).

Submitted papers must represent original unpublished research that is not
currently under review for any other conference or journal. Papers not
following these guidelines will be rejected without review and further action
may be taken, including (but not limited to) notifications sent to the heads of
the institutions of the authors and sponsors of the conference. Submissions
received after the due date, exceeding length limit, or not appropriately
structured may also not be considered. Authors may contact the conference PC
Chair for more information. The proceedings will be published through the IEEE
Computer Society Press, USA and will be made online through the IEEE Digital
Library. Selected papers from CASK 2014 will be invited to extend and submit to
the Special Issue on Many-Task Computing in the Cloud in the IEEE Transaction on
Cloud Computing (http://datasys.cs.iit.edu/events/TCC-MTC15/CFP_TCC-MTC15.pdf).

Chairs and Committees
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Workshop Co-Chairs:

* Chen Jin (Palantir Technology)
* Ioan Raicu (Illinois Institute of Technology)
* Scott Klasky (Oak Ridge National Laboratory)

Program Co-Chairs:

* Raju Vatsavai (North Carolina State Univ. & Oak Ridge National Lab.)
* Judy Qiu (Indiana University)
* George Ostrouchov (Oak Ridge National Laboratory)
* Tahsin Kurc (Stony Brook University)
* Daniel S. Katz (University of Chicago)
* Bogdan Nicolae (IBM Research)
* Doug Thain (University of Notre Dame)
* Josh Wills (Cloudera)
* Zhengzhang Chen (NEC Labs)
* Kunpeng Zhang (University of Illinois at Chicago)

-- 
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Ioan Raicu, Ph.D.
Assistant Professor, Illinois Institute of Technology (IIT)
Guest Research Faculty, Argonne National Laboratory (ANL)
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Data-Intensive Distributed Systems Laboratory, CS/IIT
Distributed Systems Laboratory, MCS/ANL
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Editor: IEEE TCC, Springer Cluster, Springer JoCCASA
Chair:  IEEE/ACM MTAGS, ACM ScienceCloud
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Cel:      1-847-722-0876
Office:   1-312-567-5704
Email:    iraicu at cs.iit.edu
Web:      http://www.cs.iit.edu/~iraicu/
Web:      http://datasys.cs.iit.edu/
LinkedIn: http://www.linkedin.com/in/ioanraicu
Google:   http://scholar.google.com/citations?user=jE73HYAAAAAJ
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