• Big-Data analytics and cloud computing: Theory, algorithms and applications

      Hill, Richard; Trovati, Marcello; Liu, Lu; Anjum, Ashiq; Zhu, Shao Ying; University of Derby (Springer, 2015)
      This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.
    • GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

      Xu, Xiaolong; Yuan, Hao; Matthew, Peter; Ray, Jeffrey; Bagdasar, Ovidiu; Trovati, Marcello; University of Derby; Nanjing University of Posts and Telecommunications, Nanjing, China; Edge Hill University, Ormskirk, UK (Springer, 2019-09-21)
      The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.
    • Guide to security assurance for cloud computing

      Zhu, Shao Ying; Hill, Richard; Trovati, Marcello; University of Derby (Springer, 2015)
      This practical and didactic text/reference discusses the leading edge of secure cloud computing, exploring the essential concepts and principles, tools, techniques and deployment models in this field. Enlightening perspectives are presented by an international collection of pre-eminent authorities in cloud security assurance from both academia and industry.
    • Influence discovery in semantic networks: An initial approach

      Trovati, Marcello; Bagdasar, Ovidiu; University of Derby (IEEE, 2014-03-26)
      Assessing the influence between concepts, which include people, physical objects, as well as theoretical ideas, plays a crucial role in understanding and discovering knowledge. Despite the huge amount of literature on knowledge discovery in semantic networks, there has been little attempt to fully classify and investigate the influence, which also includes causality, of a semantic entity on another one as dynamical entities. In this paper we will introduce an approach to discover and assess influence among nodes in a semantic network, with the aim to provide a tool to identify its type and direction. Even though this is still being developed, the preliminary evaluation shows promising and interesting results.
    • Model building

      Lowdnes, Val; Berry, Stuart; Trovati, Marcello; Whitbrook, Amanda; University of Derby; Edge Hill University (Springer, 2017-04)
    • Reduced topologically real-world networks: a big-data approach

      Trovati, Marcello; University of Derby (2015)