Call for papers

CFP in txt                                                                                                         CFP in pdf

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).


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
  • 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


Short Instructions for Authors

Accepted papers will be published by Springer in the ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING Series (AISC). Authors should submit original papers limited to a maximum of 10 formatted by using the Springer style file. Works 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 of the conference must register with a regular fee and present their work at the conference.

Authors can choose to work with word or latex; however, they are advised to use latex. Here are the style files for word or latex. Further instruction can be found at



Best papers will be selected and awarded as follows:

  • Best regular paper
  • Best student paper


This will be based on a combination of reviewers’ comments, presentations and importance and quality judged by a panel.
Best paper awards (500 Euros) are donated by the sponsor Springer Verlag, Germany and will be commemorated by a certificate.