[Apologies for cross-postings]

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The INNS Big Data conference 2015

October 23-25, 2016, Thessaloniki, Greece

EXTENDED CALL FOR PAPERS

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Homepage: http://www.innsbigdata.org

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Big data is not just about storage of and access to data. Analytics play a big role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms.
Most machine learning algorithms can't easily
scale up to big data. Plus there are challenges of high-dimensionality, velocity and variety.

The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. This type of learning is not only ideal for streaming data (as in the Industrial
Internet or the Internet of Things), but could also be used on stored big data. Neural network technologies thus can become significant components of big data analytics platforms and this inaugural INNS Conference on Big Data will begin that
collaborative adventure with big data and other learning technologies.

Thus the aim of this conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different
computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management).

Awards (?)
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* Recognizes the best paper presented at the INNS Big Data conference. Both application and theoretical papers will be considered.

* Awarded by the Big Data Analytics Section of the International Neural Network Society and is sponsored by Springer.

* The Award consists of a plaque and a $2000 honorarium ###########################################################

Important Dates
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Paper Submission May 18, 2016
Paper Decision Notification June 12, 2016
Camera Ready Submission of papers June 23, 2016
Early Registration June 20, 2016 ###########################################################

Co-Sponsors
* International Neural Network Society (INNS)
* Springer

Keynote Speakers
* Frabcesco Bonchi, Technological Center of Catalunya, Spain
* Steve Furber, University of Manchester, UK
* Rudolf Kruse, OVG University of Magdeburg, Germany
* Pitor Mirowski, Google Deep Mind, UK

Advisory Board
* Walter Freeman, University of California, Berkeley, USA
* Ali Minai, University of Cincinnati, USA
* Danil Prokhorov, Toyota Tech Center
* Theodore Trafalis, University of Oklahoma, USA
* Kumar Venayagamoorthy, Clemson University, USA
* Bernard Widrow, Stanford University, USA

General Chairs
* Plamen Angelov, Lancaster University, UK
* Yannis Manolopoulos, Aristotle University, Greece

PC Chairs
* Lazaros Iliadias, Democritus University, Greece
* Asim Roy, Arizona State University, Tempe, USA
* Marley Vellasco, PUC-Rio, Rio de Janeiro, Brazil

Special Sessions Chairs
* Alessandro Ghio, University of Genoa, Italy
* Irwin King, Chinese University of Hong Kong, China

Tutorials/Workshops Chair
* Nikola Kasabov, Auckland Universitty of Technology, New Zealand
* Bernardete Ribeiro, University of Coimbra, Portugal

Poster Session Chairs
* Yi Lu Murphy, University of Michigan-Dearborn, USA
* Liang Zhao, University of Sao Paulo, Brazil

Awards Chari
* Araceli Sanchis de Miguel, Carlos III University, Spain

Competitions Chair
Adel Alimi, University of Sfax, Tunisia

Panel Chair
* Leonid Perlovsky, Harvard University, Boston, USA

Sponsors/Exhibit Chairs
* James Dankert, BAE Systems, USA
* Rosemary Paradis, Lockheed Martin, USA

Publication Chairs
* Danilo Mandic, Imperial College, London, UK
* Mariette Awad, American University of Beirut, Lebanon

International Liaison
* De-Shuang Huang, Tongji University, Shanghai, China
* Petia Georgieva, University of Aveiro, Portugal

Publicity Chairs,
* Teng Teck Hou, Singapore Management University, Singapore
* Simone Scardapane, The Sapienza University of Rome, Italy
* Jose Antonio Iglesias Martinez, Carlos III University, Spain

Paper Submission and Publication ###########################################################
* Original works submitted as a regular paper limited to a maximum of 8 pages in IEEE 2-column format will be published in the proceedings.

* It will be peer-reviewed by at least three PC members on the basis of technical quality, relevance, originality, significance and clarity.

* At least one author of an accepted submission to the conference should register with a regular fee to present their work at the conference.

* Accepted papers will be published in the conference proceedings by Springer. ###########################################################

Topics and Areas include, but not limited to:
* Autonomous, online, incremental learning - theory, algorithms and applications in big data
* High dimensional data, feature selection, feature transformation - theory, algorithms and applications
for big data
* Scalable algorithms for big data
* Learning algorithms for high-velocity streaming data
* Big data streams analytics
* Deep neural network learning
* Machine vision and big data
* Brain-machine interfaces and big data
* Cognitive modeling and big data
* Embodied robotics and big data
* Fuzzy systems and big data
* Evolutionary systems and big data
* Evolving systems for big data analytics
* Neuromorphic hardware for scalable machine learning
* Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
* Big data and collective intelligence/collaborative learning
* Big data and hybrid systems
* Big data and self-aware systems
* Big Data and infrastructure
* Big data analytics and healthcare/medical applications
* Big data analytics and energy systems/smart grids
* Big data analytics and transportation systems
* Big data analytics in large sensor networks
* Big data and machine learning in computational biology, bioinformatics
* Recommendation systems/collaborative filtering for big data
* Big data visualization
* Online multimedia/ stream/ text analytics
* Link and graph mining
* Big data and cloud computing, large scale stream processing on the cloud

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