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Title: Big Data: Challenges,
Opportunities and
Realities
Introduction:
Big Data focuses on databases and
files with
volumes in the tera- (1012) to exabytes (1015) currently, but
trending
towards zettabytes (1018). Big data databases and files have
scaled beyond
the capacities and capabilities of commercial database
management systems.
Structured representations become a bottleneck to efficient data
storage
and retrieval. Gartner has noted four major challenges (the four
Vs): increasing
volume of data, increasing velocity (e.g., in/out and change of
data),
increasing variety of data types and structures, and increasing
variability
of data. We have suggested a fifth V: value, which is the
contribution
big data has to decision making. Add to these the increasing
number of
disciplines and problem domains where big data is having an
impact and
one sees an increase in the number of challenges and
opportunities for
big data to have a major impact on business, science, and
government.
Big Data analysis appears to be an
emerging
discipline in need of distinguishing methodologies and tools.
The challenges
and opportunities have multiplied over the past two yeas and
continue to
grow. As the plethora of data grows, new methods for processing
and understanding
this data to provide actionable information for decision-makers
are required
that match the domain knowledge and problems of fields with
specific missions
and constraints. New metrics are required to assess its impact
on decision-making
in each domain where success is defined in mission based terms.
This mini track is soliciting paper
submissions
that: advance our knowledge of Big Data storage and structure;
help us
learn about effective processes and approaches to effectively
manage Big
Data and the associated analytics; and begin to identify ways to
measure
the organizational benefits derived from using and analyzing Big
Data.
Papers will be solicited in several areas, including, but not
limited to
the following:
• Innovative
structures and techniques for big data representation (including
RDF, RDFS,
audio, image, video, etc.)
• Graph
analytics – both syntactic and semantic
• Business
Analytics – to include business intelligence as it uses big
data
• Advanced
analytics, including applications of the MapReduce and
Message-Passing
Interface (MPI) paradigms for implementing analytics
• Mechanisms
for annotating big data with semantic information
• Scalable
semantic reasoning across big data stores
• Challenges
in using and analyzing big data
• Case
Studies of big data implementations
• Innovative
visualization algorithms and techniques for big data
• Challenges
in managing big data repositories and projects using emerging
tools and
accessing such repositories using new languages (such as Pig,
Jaql, etc.)
• Metrics
for assessing the impact of big data in business, scientific,
and governmental
decision-making.
If you have any questions, please
contact
the primary co-chair.
HICSS-47 offers a unique, highly
interactive
and professionally challenging environment that attendees find
"very
helpful -- lots of different perspectives and ideas as a result
of discussion."
HICSS sessions are comprised primarily of refereed paper
presentations;
the conference does not host vendor presentations. HICSS is
sponsored by
the Shidler College of Business a the University of Hawai’i at
Manoa and
the IEEE Computer Society.
CoChairs:
Stephen Kaisler,
skaisler1@comcast.net, Primary
Co-chair
Frank Armour, fjarmour@gmail.com
Alberto Espinosa, alberto@american.edu
William H. Money, wmoney@gwu.edu