Big Data Analytics Section

Chair: vacant

Motivation

Although a nascent technology, companies across the globe have rapidly increased their investments in big data technology in the last few years. Big data is also a growing research area and national science organizations and defense agencies in many countries are funding some of the research. Big data is not just about storage and access of data in a distributed framework. Analytics play a big role in trying to make sense of that data. Although many analytics platforms have been developed and deployed for big data, analytics will remain a challenge for years to come.

The neural network field should and can be an important player in big data and lead some of its technological developments. Our strength is in our decades of work on online learning, a form of learning that has been under-researched, until recently, by other related fields of analytics such as statistics, machine learning, and data mining. And our online learning is not only a perfect match for streaming data (as in sensor data of the Industrial Internet or the Internet of Things) but could also be used on stored big data. Our learning technologies are already configured to take advantage of parallel computations. We also have neuromorphic hardware that can deliver the millisecond or nanosecond speed of computations required in many big data applications such as the Industrial Internet. Therefore, we are in a position where we can quickly develop and deploy some of our mature technologies to solve big data learning problems. Big data has the potential to quickly become the most successful application area of neural networks.

The purpose and goal of this Section are to help the neural network field position itself as the leading technology provider for big data analytics.

Objectives

The main objective of the Section on BDA is to be a focal point of scientific and networking activities in this emerging area. More specifically, the Section and its members will:

Have regular (annual) conferences;
Organize workshops, special sessions, panels, tutorials at other high profile conferences
Engage the leading industry including in plenary talks, sponsorship, etc.
Organize special issues in leading journals
Actively contribute with papers, chapters, books on BDA and Neural Networks
Organize and sponsor BDA competitions
Initiate and maintain an e-Newsletter on BDA and Neural Networks involving the leading industry representatives

Structure

The BDA Section was established in 2014 and was chaired by Asim Roy (Professor at Arizona State University, USA) till August 2015. The leadership of the BDA Section was taken in August 2015 (Board of Government decision, July 2015) by Plamen Angelov (Professor at Lancaster University, UK). Asim switched to the role of co-Chair responsible for membership.

Former co-chairs include:

John Weng, Michigan State University, USA (publications)
Kumar Venayagamoorthy, Clemson University, SC, USA (industrial liaison)
Marley Vellasco, Pontifical University, Rio de Janeiro, Brazil (conferences)
Theodore Trafalis, University of Oklahoma, USA (publicity)
Leonid Perlovsky, Harvard University, Boston, USA (industrial liaison)
Nikola Kasabov, Auckland University of Technology, New Zealand (initiatives)
Adel Alimi, University of Sfax, Sfax, Tunisia (publicity)

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