Advances in Core Computer Science-Based Technologies

  • George A. TsihrintzisEmail author
  • Maria Virvou
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 14)


At the dawn of the 4th Industrial Revolution, the field of computer science-based technologies is growing continuously and rapidly, developing in both itself and towards applications of many other disciplines. The book at hand aims at exposing its reader to some of the most significant advances in core computer science-based technologies. As such, the book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent computer science-based technologies. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into the application areas of interest to him/her.


  1. 1.
    J. Toonders, Data is the new oil of the digital economy. Wired.
  2. 2.
    K. Schwabd, The fourth industrial revolution—what it means and how to respond, Foreign Affairs, December 12, 2015.
  3. 3.
    G.A. Tsihrintzis, D.N. Sotiropoulos, L.C. Jain (eds.), Machine Learning Paradigms—Advances in Data Analytics, volume 149 in Intelligent Systems Reference Library Book Series, Springer 2018Google Scholar
  4. 4.
    G.A. Tsihrintzis, M. Virvou, E. Sakkopoulos, L.C. Jain (eds.), Machine Learning Paradigms—Applications of Learning and Analytics in Intelligent Systems, volume 1 in Learning and Analytics in Intelligent Systems Book Series, Springer 2019Google Scholar
  5. 5.
    K. Chrysafiadi, M. Virvou, Advances in Personalized Web-based Education, volume 78 in Intelligent Systems Reference Library Book Series, Springer 2015Google Scholar
  6. 6.
    M. Virvou, E. Alepis, G.A. Tsihrintzis, L.C. Jain, Machine Learning Paradigms—Advances in Learning Analytics, volume 158 in Intelligent Systems Reference Library Book Series, Springer 2020Google Scholar
  7. 7.
    E. Alepis, M. Virvou, Object-Oriented User Interfaces for Personalized Mobile Learning, volume 64 in Intelligent Systems Reference Library Book Series, Springer 2014Google Scholar
  8. 8.
    S. Bahri, N. Zoghlami, M. Abed, J. Manuel, R.S. Tavares, Big data for healthcare: a survey. IEEE Access 7, 7397–7408 (2019)Google Scholar
  9. 9.
    Qiong Cai, Hao Wang, Zhenmin Li, Xiao Liu, A survey on multimodal data-driven smart healthcare systems: approaches and applications. IEEE Access 7, 133583–133599 (2019)CrossRefGoogle Scholar
  10. 10.
    B.H. Thomas, A survey of visual, mixed, and augmented reality gaming. Comput. Entertain 10(1), 3:1–3:33 (2012)Google Scholar
  11. 11.
    Peihai Zhao, Zhijun Ding, Mimi Wang, Ruihao Cao, Behavior analysis for electronic commerce trading systems: a survey. IEEE Access 7, 108703–108728 (2019)CrossRefGoogle Scholar
  12. 12.
    B. Yoo, M. Jang, A bibliographic survey of business models, service relationships, and technology in electronic commerce. Electr. Commer. Res. Appl. 33 (2019)Google Scholar
  13. 13.
    Hongbo Jiang, Chao Cai, Xiaoqiang Ma, Yang Yang, Jiangchuan Liu, Smart home based on WiFi sensing: a survey. IEEE Access 6, 13317–13325 (2018)CrossRefGoogle Scholar
  14. 14.
    Du Rong, Paolo Santi, Ming Xiao, Athanasios V. Vasilakos, Carlo Fischione, The sensable city: a survey on the deployment and management for smart city monitoring. IEEE Commun. Surv. Tutor. 21(2), 1533–1560 (2019)CrossRefGoogle Scholar
  15. 15.
    B.P.L. Lau, M.S. Hasala, Y. Zhou, N.U. Hassan, C. Yuen, M. Zhang, U.-X. Tan, A survey of data fusion in smart city applications. Inf. Fus. 52, 357–374 (2019)Google Scholar
  16. 16.
    X. Liu, J. Cao, Y. Yang, S. Jiang, CPS-based smart warehouse for industry 4.0: a survey of the underlying technologies. Computers 7(1), 13 (2018)Google Scholar
  17. 17.
    M.M. Dhanvijay, S.C. Patil, Internet of Things: A survey of enabling technologies in healthcare and its applications. Comput. Netw. 153, 113–131 (2019)Google Scholar
  18. 18.
    N. Panwar, S. Sharma, S. Mehrotra, L. Krzywiecki, N. Venkatasubramanian, Smart home survey on security and privacy, (2019),
  19. 19.
    M. Khawla, T. Mazri, A survey on the security of smart homes: issues and solutions, ICSDE 2018, pp. 81–87Google Scholar
  20. 20.
    David Eckhoff, Isabel Wagner, Privacy in the smart city—applications, technologies, challenges, and solutions. IEEE Commun. Surv. Tutor. 20(1), 489–516 (2018)CrossRefGoogle Scholar
  21. 21.
    Abdullah Algarni, A survey and classification of security and privacy research in smart healthcare systems. IEEE Access 7, 101879–101894 (2019)CrossRefGoogle Scholar
  22. 22.
    Omer Tene, Katrine Evans, Bruno Gencarelli, Gabe Maldoff, Gabriela Zanfir-Fortuna, GDPR at year one: enter the designers and engineers. IEEE Secur. Priv. 17(6), 7–9 (2019)CrossRefGoogle Scholar
  23. 23.
    Paul Breitbarth, The impact of GDPR one year on. Netw. Secur. 7, 11–13 (2019)CrossRefGoogle Scholar
  24. 24.
    A. Brombacher, Quality, reliability, data, and the impact of the GDPR …. Qual. Reliab. Eng. Int. 35(4), 869 (2019)Google Scholar
  25. 25.
    D. Huth, F. Matthes, “Appropriate technical and organizational measures”: identifying privacy engineering approaches to meet GDPR requirements, in The 25th Americas Conference on Information Systems (AMCIS 2019), Cancún International Convention Center, Cancún, México, August 15–17, 2019Google Scholar
  26. 26.
    D. Rösch, T. Schuster, L. Waidelich, S. Alpers, Privacy control patterns for compliant application of GDPR, in The 25th Americas Conference on Information Systems (AMCIS 2019), Cancún International Convention Center, Cancún, México, August 15–17, 2019Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of PiraeusPiraeusGreece

Personalised recommendations