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7th EAI International Conference on Big Data Technologies and Applications

November 17–18, 2016 | Seoul, South Korea


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Registration for BDTA 2016 is open. Click here to Register


Welcome to BDTA 2017, the 7th EAI International Conference on Big Data Technologies and Applications!

The conference will be held on November 17-18, 2017 at Chung-Ang University in Seoul, Korea.
This edition of the BDTA conference continues to focus on issues on Computational Intelligence, Big Data, and Data Mining.
We extend a warm welcome to delegates from industry and academia to join us for two exciting days of new ideas dissemination and technical discussions.

Our Organizing Committee co-chaired by Prof. Pankoo Kim and Prof.
Jason J. Jung has assembled an excellent technical program consisting of around 20 technical papers, including several papers invited from top researchers in the field.

Highlights of the conference will include 7 keynote presentations by Prof. Francesco Piccialli, Dr. Myunggwon Hwang, Mr. Hyun-Jun Kim, Prof. Marc Cavazza, Prof. Adam Jatowt, Prof. Pablo Gervás, and Prof. Jason J. Jung, who will talk about their amazing experiences.

On behalf of the Organizing Committee of BDTA 2017, we would like to thank all the authors who have contributed their valuable work to the conference, and the TPC members and reviewers for maintaining the quality of the technical program. We would also like to thank the keynote speaker for contributing their time, knowledge and wisdom. This conference is endorsed and organized by Chung-Ang University, Chosun University, and Yeungnam University.
We are particular grateful to the local staffs who have tirelessly looked after every little details in making this conference possible.

We look forward to meeting and greeting you at BDTA 2017 in Seoul!

Jason J. Jung

Chung-Ang University, Seoul, South Korea




See the latest announcement of BDTA in the EAI newsletter



Due to the explosive evolution of Information Technology and Computer Science, we have entered in the Big Data Age, and this is really a scientific revolution, not just a fashion. As always, the technological aspects evolve faster than the scientific community mentality. Transforming Big Data into Big Knowledge and developing a new kind of Knowledge-Based Systems require new visions and approaches. Companies, facing the Big Data challenges, are moving faster in the right direction than the scientific community, being under a stronger competitive pressure. They were forced to renounce to wishful thinking, like the idea that a few variables, embedded in a few rules, discovered using the old fashion statistics, will give intelligent support for business decisions. We have to do the same for developing various applications.

Moreover, motivated by the big data analytics needs, new computing and storage technologies are developing rapidly and pushing for new high-end hardware geared toward big data problems. While the high performance computing technologies have the potential to greatly improve effectiveness of big data analytics, the cost and sophistications of those technology and limited initial application support often make them inaccessible to the end users and not fully utilized in academia years later. Meanwhile, comprehensive analytic software environment and platforms, such as R and Python, have become increasingly popular open-source platforms for data analysis.

Also, Computational Intelligence (CI) methodologies, tailored to Big Data, and combined with a proper vision of living systems, e.g., as complex dynamical systems or networks of interacting entities, could pave the way to Knowledge-Based System.


Potential topics of interest, which can be investigated from different perspectives (social, organizational, technological) include, but are not limited to, the following application domains:

·     Data Visualization and Visual Analytics

·     Natural Language Processing in Big Texts

·     Biomedical imaging pre-processing and Analysis

·     Hardware and Software solutions for Big Data Searching, Storing and Management

·     Structured and Unstructured Data/Text/Web Mining

·     Deep Learning architecture, representations, unsupervised and supervised algorithms

·     Scalable computational intelligence tools

·     Novel Computational Intelligence approaches for data analysis

·     Evolutionary and Bio-inspired approaches for Big Data analysis

·     New domains and novel applications related to Big Data technologies 


Paper Submission

Regular papers should be no more than 10 pages in length. Submissions will be reviewed anonymously by at least three expert reviewers. Papers will be judged on originality, correctness, clarity and relevance. Submission of the paper implies agreement of the author(s) to attend the conference and present the paper if accepted. Full details of submission procedures are available here.


All presented papers in the conference will be published in the proceedings of the conference.

Accepted papers will be published in the BDTA Conference Proceedings and by Springer-Verlag in the Lecture Notes of ICST (LNICST). The proceedings will be available both in book form and via the SpringerLink digital library, which is one of the largest digital libraries online and covers a variety of scientific disciplines.

The proceedings are submitted for inclusion to the leading indexing services: Elsevier (EI), Thomson Scientific (ISI), Scopus, Crossref, Google Scholar, DBLP. Best papers will be invited to publish in special issues:


  • The event is endorsed by the European Alliance for Innovation, a leading community-based organisation devoted to the advancement of innovation in the field of ICT.

  • All accepted papers will be submitted for publication in Springer and made available through SpringerLink Digital Library, one of the world's largest scientific libraries.