Data Science: New Issues, Challenges and Applications

  • Gintautas Dzemyda
  • Jolita Bernatavičienė
  • Janusz Kacprzyk

Part of the Studies in Computational Intelligence book series (SCI, volume 869)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Egor S. Ivanov, Aleksandr V. Smirnov, Igor P. Tishchenko, Andrei N. Vinogradov
    Pages 1-16
  3. Vytautas Kaminskas, Edgaras Ščiglinskas
    Pages 17-41
  4. Maria Visan, Angela Ionita, Florin Gheorghe Filip
    Pages 97-110
  5. Saulius Gudas, Andrius Valatavičius
    Pages 111-143
  6. Algirdas Lančinskas, Pascual Fernández, Blas Pelegrín, Julius Žilinskas
    Pages 145-163
  7. Povilas Treigys, Gražina Korvel, Gintautas Tamulevičius, Jolita Bernatavičienė, Bożena Kostek
    Pages 165-181
  8. Krzysztof Kąkol, Gražina Korvel, Bożena Kostek
    Pages 199-218
  9. Dalius Mažeika, Jevgenij Mikejan
    Pages 219-234
  10. Tatjana Sidekerskienė, Robertas Damaševičius, Marcin Woźniak
    Pages 235-252
  11. Florin Gheorghe Filip
    Pages 253-277
  12. Andrius Valatavičius, Saulius Gudas
    Pages 279-296
  13. Marius Liutvinavicius, Virgilijus Sakalauskas, Dalia Kriksciuniene
    Pages 297-313

About this book


This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science.
Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field.
In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others. 


Data Science Computational Intelligence Intelligent Systems Computational Thinking Intelligent Decision Support Systems

Editors and affiliations

  • Gintautas Dzemyda
    • 1
  • Jolita Bernatavičienė
    • 2
  • Janusz Kacprzyk
    • 3
  1. 1.Institute of Data Science and Digital TechnologiesVilnius UniversityVilniusLithuania
  2. 2.Institute of Data Science and Digital TechnologiesVilnius UniversityVilniusLithuania
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Bibliographic information

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