[Storage-research-list] The First Workshop on Mission-Critical Big Data Analytics (MCBDA 2016)

[Sunita Chandrasekaran]

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The First Workshop on Mission-Critical Big Data Analytics (MCBDA 2016)

The Mission-Critical Big Data Analytics workshop (MCBDA 2016) is intended
to serve as a forum for discussion of all aspects of current research and
development in big data analytics, especially in support of
mission-critical applications.

The big data era has led to a wealth of fundamental and practical research
that focuses on solving the challenges involved in analyzing massive
volumes of complex data from a variety of sources. Furthermore, there is a
growing demand for real-time stream data analytics to support
mission-critical applications in military, healthcare, retail, security,
and other important sectors. A big data analytics platform is needed that
is able to continuously process streaming data from smart sensors, cameras,
and other devices and to help make real-time decisions for mission-critical
applications. This requirement, in turn, is driving innovation in areas
such as computing platform design, programming models, communications,
IoT/sensor networks, and algorithms. The objective of this workshop is to
bring together researchers to discuss the state of the art and potential
innovations in these fields, and to publish findings that address the big
data analytics challenges.

Authors are invited to submit work that is related to the theme of this
conference, as described below. The workshop program will include submitted
presentations on cutting edge research topics in big data, keynote
speeches, and tutorial sessions with hands-on training. These will be
accompanied by a poster session and a demo session where students will
present their work.

This two-day event will take place on May 16-17, 2016 at Prairie View A&M
University. Part of the Texas A&M University System, Prairie View is an
HBCU located near Houston, Texas.
Topics of Interest

Topics of the workshop include, but are not limited to, the following.
1.     Architecture
a.     Big Data Analytics Platforms
b.     Cloud Computing for Big Data Analytics
c.      Heterogeneous Cloud Platform with GPU, APU, FPGA
d.     Dynamic resource provisioning
e.     Big data storage architecture
2.     Programming Models
a.     New scalable programming models for big data analytics
b.     Spark / MapReduce Performance characterization and optimization
c.      Spark / MapReduce on Heterogeneous computing systems
d.     Extensions of Spark/MapReduce
e.     Debugging and Performance Tools
3.     Algorithms
a.     Machine Learning algorithms
b.     Data Mining algorithms
c.      Statistical methods
d.     Graph algorithms
e.     Big data querying and search
4.     Applications
a.     Mission-critical big data analytics applications
b.     Stream processing/analytics applications
c.      Data-intensive applications using Spark/MapReduce
d.     Real-time applications
e.     Image and video analytics
5.     Big Data Collection and Aggregation:
a.     Big data collection in IoT/sensor networks
b.     Big data processing schemes in IoT/sensor networks
c.      Mobile and cloud support for IoT/sensor networks
d.     Security and privacy in IoT/sensor networks
e.     Energy efficiency of IoT/sensor networks
6.     HPC and Big Data
a.     Convergence of HPC and Big Data Frameworks
b.     HPC programming models for Big Data Applications
c.      Performance optimizations for Big Data Systems and Applications
d.     Performance Modeling for Big Data Computing
e.     Scientific Computing with Big Data
f.      HPC and Big Data Education
Important dates
·      Paper submission: March 31st, 2016
·      Notification of acceptance: April 20th, 2016
·      Camera-ready paper: April 30th, 2016
·      Workshop: May 16th-17th, 2016



*STEERING COMMITTEE*

Cajetan Akujuobi, PVAMU
Evelyn Kent, OSD/DOD
Jie Liu, Microsoft Research

*ORGANIZING COMMITTEE*

*General Chairs*

Barbara Chapman, Stony Brook University
Timothy S. Kroecker, AFRL

*Technical Program Chairs*

Alex Aved, AFRL
Lijun Qian, PVAMU
Lei Huang, PVAMU

TECHNICAL PROGRAM COMMITTEE
Sikha Bagui, University of West Florida, USA
Sunita Chandrasekaran, University of Delaware, USA
Xiaoming Li, University of Delaware, USA
Chunhua Liao, Lawrence Livermore National Laboratory, USA
Yongchao Liu, Georgia Tech, USA
Mariofanna Milanova, University of Arkansas, USA
Shishir Shah, University of Houston, USA
Yonghui Wang, Prairie View A&M University, USA
Dalei Wu, University of Tennessee, USA
Yonggao Yang, Prairie View A&M University, USA


*LOCAL ORGANIZATION COMMITTEE *

*Yonggao Yang (Publications) *
Xiangfang Li (Tutorials)
Pamela Obiomon (Student Posters)
*John Fuller (Local Arrangements)*
*Paul Potier (Industry Contacts)*
*Yonghui Wang (Publicity)*
*Lin Li (Publicity)*
*Suxia Cui (Finances)*
*Yuzhong Yan (Registration)*



Paper Submission Guidelines
Submissions may not exceed 4 pages in PDF format including figures
and references, and must be formatted in the 2-column IEEE format.
Submitted papers must be original work that has not appeared in, and is not
under consideration for, another conference or journal. Work in progress is
welcome, but preliminary results should be made available as a proof of
concept.  Accepted papers will be printed in the conference proceedings.

Selected authors will be invited to submit full-length papers for inclusion
in a Special Issue of one of the leading journals in this field.

Templates are available on the IEEE website:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html

Submissions are to be uploaded at EasyChair:
http://easychair.org/conferences/?conf=mcbda2016

Registration

The workshop registration fee is $200, including 2 breakfast and 2 lunches.
This workshop is FREE for students (registration is required; current
student ID is required on site; breakfast and lunch not included).

Please register at:
https://www.eventbrite.com/e/the-first-workshop-of-mission-critical-big-data-analytics-mcbda-2016-tickets-20587633165

Contact
For further information, contact Prof. Lei Huang, Prairie View A&M
University: lhuang at pvamu.edu
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