<font size=3 face="Arial">Title: Big Data: Challenges, Opportunities and
Realities</font>
<br>
<br><font size=3 face="Arial">Introduction:</font>
<br>
<br><font size=3 face="Arial">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.</font>
<br>
<br><font size=3 face="Arial">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.</font>
<br>
<br><font size=3 face="Arial">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: </font>
<br>
<br><font size=3 face="Arial">• Innovative
structures and techniques for big data representation (including RDF, RDFS,
audio, image, video, etc.)</font>
<br><font size=3 face="Arial">• Graph
analytics – both syntactic and semantic</font>
<br><font size=3 face="Arial">• Business
Analytics – to include business intelligence as it uses big data</font>
<br><font size=3 face="Arial">• Advanced
analytics, including applications of the MapReduce and Message-Passing
Interface (MPI) paradigms for implementing analytics</font>
<br><font size=3 face="Arial">• Mechanisms
for annotating big data with semantic information</font>
<br><font size=3 face="Arial">• Scalable
semantic reasoning across big data stores</font>
<br><font size=3 face="Arial">• Challenges
in using and analyzing big data</font>
<br><font size=3 face="Arial">• Case
Studies of big data implementations</font>
<br><font size=3 face="Arial">• Innovative
visualization algorithms and techniques for big data</font>
<br><font size=3 face="Arial">• Challenges
in managing big data repositories and projects using emerging tools and
accessing such repositories using new languages (such as Pig, Jaql, etc.)</font>
<br><font size=3 face="Arial">• Metrics
for assessing the impact of big data in business, scientific, and governmental
decision-making.</font>
<br>
<br><font size=3 face="Arial">If you have any questions, please contact
the primary co-chair.</font>
<br>
<br><font size=3 face="Arial">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.</font>
<br>
<br><font size=3 face="Arial">CoChairs: </font>
<br>
<br><font size=3 face="Arial">Stephen Kaisler, skaisler1@comcast.net, Primary
Co-chair</font>
<br><font size=3 face="Arial">Frank Armour, fjarmour@gmail.com</font>
<br><font size=3 face="Arial">Alberto Espinosa, alberto@american.edu</font>
<br><font size=3 face="Arial">William H. Money, wmoney@gwu.edu</font>
<br>