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Big Data Analytics@IJCNN 2017 : Special Session: 'Large Datasets and Big Data Analytics: Theory, Methods, and Applications' at IJCNN 2017


When May 14, 2017 - May 19, 2017
Where Anchorage, Alaska, USA
Submission Deadline Nov 15, 2016
Notification Due Jan 20, 2017
Final Version Due Feb 20, 2017
Categories    computer science   artificial intelligence   machine learning   data mining

Call For Papers

[Apologies if you receive multiple copies of this CFP]

Call for papers: special session on "Large Datasets and Big Data Analytics: Theory, Methods, and Applications" at IJCNN 2017

International Joint Conference on Neural Networks (IJCNN 2017).
14-19 May 2017, Anchorage, Alaska, USA -

The information age brings along an exponentially growing quantity of heterogeneous data from multiple sources in every aspect of our lives: data coming from social networks, internet of things, experiments in biology research and data from transportation systems are only a few examples. Recent trends in the area suggest that in the coming years the exponential data growth will continue, and that there is a strong need to find efficient solutions to deal with aspects such as data wrangling, real-time processing, information extraction and abstract model generation.
Large datasets and big data analytics is the area of research focused on collecting, examining and processing large multi-structure, multi-modal, and multi-source datasets in order to discover patterns, correlations and extract information from data. In order to be able to perform such an analysis, conventional technologies and machine learning theory and algorithms are not directly applicable because they are not able to deal efficiently and effectively with such amount of data. Thus, specific techniques have to be developed.
The purpose of this special session is to highlight recent advances in the field of large datasets and big data analytics. In particular, this session welcomes contributions toward both the development of new machine learning methods and the improvement of already available tools suited for big data analysis. We also encourage the submission of new theoretical results in the Statistical Learning Theory framework and innovative solutions to real world problems.
In particular, topics of interest include, but are not limited to:
- Statistical Learning Theory for Large Datasets;
- Big Data Technologies;
- Learning on data Streams;
- Deep Learning for Large Datasets;
- Scalable Machine Learning for Structured Data;
- Scalable Kernel Methods for Large Datasets;
- Recommender Systems for Large Datasets;
- Big Data for Smart Cities and Transportation;
- Big Social Data Analysis;
- Big Data for Cybersecurity;
- Big Data in Bioinformatics and Healthcare;
- Big Data in the Internet of Things.

Prospective authors must submit their paper through the IJCNN portal following the instructions provided in Each paper will undergo a peer reviewing process for its acceptance.

Paper submission deadline : 15 November 2016
Notification of acceptance : 20 January 2017
Camera-ready submission: 20 February 2017
The IJCNN 2017 conference : 14-19 May 2017

Luca Oneto, University of Genoa (Italy),
Nicolò Navarin, University of Padua (Italy),
Michele Donini, Istituto Italiano di Tecnologia (Italy),
Fabio Aiolli, University of Padua (Italy),
Davide Anguita, University of Genoa (Italy),

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