Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks

Building consensus in strategic decision-making : system dynamics as a group support system

Contains fulltext : 28724.pdf (publisher's version ) (Open Access)System dynamics was originally founded as a method for modeling and simulating the behavior of industrial systems. In recent years it is increasingly employed as a Group Support System for strategic decision-making groups. The model is constructed in direct interaction with a management team, and the procedure is generally referred to as group model-building. The model can be conceptual (qualitative) or a full-blown (quantitative) computer simulation model. In this article, a case is described in which a qualitative system dynamics model was built to support strategic decision making in a Dutch government agency. Since people from different departments held strongly opposite viewpoints on the strategy, the agency had discussed its strategic problem for more than a year, but was obviously not able to reach consensus. The application of group model-building was successful in integrating opposite points of view, as well as in fostering consensus and creating commitment. The purpose of the article is twofold: first, to illustrate the process of group model-building with system dynamics; second, to evaluate why it was successful. Evaluation results reveal the importance of both systemic thinking through model-building and the role of the facilitator in catalyzing the strategic decision-making process