• Analysis of Queueing Networks in Equilibrium: Numerical Steady-State Solutions of Markov Chains

      Lokshina, Izabella V.; Lanting, Cees J. M. (IGI Global, 2020)
      Equilibria of queueing networks are a means for performance analysis of real communication networks introduced as Markov chains. In this paper, the authors developed, evaluated, and compared computational procedures to obtain numerical solutions for queueing networks in equilibrium with the use of direct, iterative, and aggregative techniques in steady-state analysis of Markov chains. Advanced computational procedures are developed with the use of Gaussian elimination, power iteration, Courtois’ decomposition, and Takahashi’s iteration techniques. Numerical examples are provided together with comparative analysis of obtained results. The authors consider these procedures are also applicable to other domains where systems are described with comparable queuing models and stochastic techniques are sufficiently relevant. Several suitable domains of applicability are proposed.
    • A Study on Wide-ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents during COVID-19?

      Lokshina, Izabella V.; Lanting, Cees J. M. (IGI Global, 2021)
      Exponential growth in the commercial use of the internet has dramatically increased the volume and scope of data gathered and analyzed by datacentric business organizations. Big Data emerged as a term to summarize both the technical and commercial aspects of these growing data collection and analysis processes. Formerly, much discussion of Big Data was focused on its transformational potential for technological innovation and efficiency; however, less attention was given to its ethical implications beyond the generation of commercial value. In this paper, the authors investigate the wide-ranging ethical implications of Big Data technology in a digital society. They inform that strategies behind Big Data technology require organizational systems, or business ecosystems, that also leave them vulnerable to accidents associated with its commercial value and known as data accidents. These data accidents have distinct features and raise important concerns, including data privacy during COVID-19. The authors suggest successful risk mitigation strategies.