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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
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: