BigData 2014 - Big Data: Challenges, Opportunities and Realities

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-AT-comcast.net, Primary Co-chair
Frank Armour, fjarmour-AT-gmail.com
Alberto Espinosa, alberto-AT-american.edu
William H. Money, wmoney-AT-gwu.edu