Scope

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 the 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.

Topics

We welcome contributions from the following fields:

    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
    Education Data Mining and Visualization
    Educational Big Data and Learning Analysis
    Artificial Intelligence in Education
    Big Data and Intelligent Learning Environment
    Deep Learning Application in Education

Publication

All accepted and presented papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library.

Past volumes of BDTA proceedings are indexed in leading indexing services, including Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).

Authors of selected best accepted and presented papers will be invited to submit an extended version to:

    Mobile Networks and Applications (MONET) Journal (IF: 2.390)
    EAI Endorsed Transactions on Scalable Information Systems

Get published with EAI:

    EAI Transactions (Open Access)
    EAI/Springer Innovations in Communications and Computing Book Series