Data Analysis Services Related to the IoT and Big Data: Potential Business Opportunities for Third Parties

The Internet of Things (IoT) provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. \ \ In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services (IoS). These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. \ \ Different business models may support opportunities that generate revenue and value for various types of customers. \ \ This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential opportunities

Evolution of regulatory models for public health data ecosystems from a linked democracy perspective

Public healthcare is a data-intensive environment that manages ever-increasing volumes of biomedical data resulting from medical data-generating technologies. In this paper, the authors discuss strategies to regulate the collection and use of biomedical data and metadata to build sustainable public health data ecosystems; this can assist citizens to get control of dataflows by defining identity in the public domain and shaping the capacity to use the web of data: get access to healthcare services and receive benefits and appropriate care. The authors suggest that a strategy based on the linked democracy governance model and safeguards, implemented through the meta-rule of law, enables better design of regulatory tools to handle semantically driven data flows. This strategy ties well in with models of deliberative and epistemic democracy, focused on relationships between people, data, and institutions. The authors investigate privacy, security, and data protection issues, applying existing ethical and legal frameworks for public health data and the theory of justice; they discuss the implementation of strategies to articulate the public domain and propose intermediate, anchoring institutions at the meso-level by building ontologies, selecting technical functionalities and algorithms, and embedding protections of the rule of law into specific public health data ecosystems

Accelerated Buffer Overflow Simulation in Self-Similar Queuing Networks with Long-Range Dependent Processes and Finite Buffer Capacity

The paper recommends an approach to estimate effectively the probability of buffer overflow in high-speed communication networks, capable of carrying diverse traffic, including self-similar teletraffic, and supporting diverse levels of quality of service. Simulations with stochastic, long-range dependent self-similar traffic source models are conducted. A new efficient algorithm, based on a variant of the RESTART/LRE method, is developed and applied to accelerate the buffer overflow simulation in a finite buffer single server model under long-range dependent self-similar traffic load with different buffer sizes. Numerical examples and simulation results are show

Advances and Challenges in Convergent Communication Networks

Web of Science9644927491