• Addressing Ethical Concerns of Big Data as a Prerequisite for a Sustainable Big Data Industry

      Lokshina, Izabella V.; Lanting, Cees J. M. (IGI Global, 2018)
      Big Data combines information from diverse sources to create knowledge, make better predictions and tailor services. This article analyzes Big Data both as a technology and an industrial activity, and identifies the points of weakness and ethical concerns created by current business practices. Potential solutions for these concerns are proposed in order to build and maintain business practices respecting ethical standards as a prerequisite for a sustainable Big Data industry. This article covers both the usage of Big Data by industry and the development of a sustainable Big Data services industry.
    • 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.
    • IoT- and Big Data-Driven Data Analysis Services for Third Parties, Strategic Implications and Business Opportunities

      Lokshina, Izabella V.; Lanting, Cees J. M.; Durkin, Barbara J. (IGI Global, 2018)
      This article describes ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, now cut across every area of modern day living, affecting individuals and businesses and offering the ability to obtain and measure environmental indicators. Proliferation of these devices in a communicating-actuating network creates an Internet of Things (IoT). The IoT provides the tools to establish a major, global data-driven ecosystem that also enables Big Data techniques to be used. New business models may focus on the provision of services, i.e., the Internet of Services (IoS). These models assume the presence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated. Different business models may support opportunities to create revenue and value for various types of customers. This article contributes to the literature by considering, a first, knowledge-based management practices, business models, strategic implications and business opportunities for third-party data analysis services.
    • Qualitative Evaluation of IoT-Driven eHealth: KM, Business Models, Deployment and Evolution

      Lokshina, Izabella V.; Lanting, Cees J. M. (IGI Global, 2018)
      This article explains that eHealth has major potential, and its adoption may be considered necessary to achieve increased ambulant and remote medical care, increased quality, reduced personnel needs, and reduced costs potential in healthcare. In this paper, the authors try to give a reasonable, qualitative evaluation of IoT-driven eHealth from theoretical and practical viewpoints. They look at associated knowledge management issues and contributions of IoT to eHealth, along with requirements, benefits, limitations and entry barriers. Important attention is given to security and privacy issues. Finally, the conditions for business plans and accompanying value chains are realistically analyzed. The resulting implementation issues and required commitments are also discussed. The authors confirm that IoT-driven eHealth can happen and will happen; however, much more needs to be addressed to bring it back in sync with medical and general technological developments in an industrial state-of-the-art perspective and to recognize and get timely the benefits.
    • 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.