By Hyunjoung Lee, Il Sohn
Fundamentals of huge info community research for learn and Industry
Hyunjoung Lee, Institute of eco-friendly know-how, Yonsei collage, Republic of Korea
Il Sohn, Material technology and Engineering, Yonsei collage, Republic of Korea
Presents the technique of massive facts research utilizing examples from learn and industry
There are quite a lot of information in all places, and the facility to select the most important details is more and more very important. opposite to well known trust, now not all info turns out to be useful; mammoth information community research assumes that facts isn't just huge, but additionally significant, and this e-book makes a speciality of the elemental suggestions required to extract crucial details from monstrous datasets.
Featuring case reports drawn mostly from the iron and metal industries, this ebook deals functional assistance in order to let readers to simply comprehend titanic information community research. specific cognizance is paid to the technique of community research, delivering details at the approach to info assortment, on study layout and research, and at the interpretation of results. various courses together with UCINET, NetMiner, R, NodeXL, and Gephi for community research are lined in detail.
Fundamentals of massive facts community Analysis for learn and Industry appears to be like at monstrous facts from a clean standpoint, and offers a brand new method of facts analysis.
Fundamentals of huge facts community Analysis for study and Industry will end up a worthwhile source for analysts, learn engineers, business engineers, advertising and marketing execs, and any contributors facing collected huge info whose curiosity is to investigate and establish capability relationships between information sets.
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Extra info for Fundamentals of Big Data Network Analysis for Research and Industry
2016 John Wiley & Sons, Ltd. Published 2016 by John Wiley & Sons, Ltd. 36 Fundamentals of Big Data Network Analysis depending on the relevant subject and context. For example, role relationships are exhibited by exporting and importing countries, manufacturing companies and companies supplying raw materials, and by students and teachers. There are also cognitive or emotional relationships that are established on the basis of factors such as acknowledgement, friendship, respect, and exclusivity.
International AAAI Conference on Weblogs and Social Media. , Shneiderman, B. et al. (2010) NodeXL: A Free and Open Network Overview, Discovery and Exploration Add‐In for Excel 2007/2010, http://nodexl. org (accessed 22 May 2015). 3 Understanding Network Analysis While existing research is based on an actors’ independence, network analysis assumes that the actors are interdependent. Based on that assumption, network analysis aims to quantitatively measure regular patterns. Network analysis assumes that the actions of actors are not independent or autonomous, but interdependent, where the relationship among the actors works as a conduit for flow or transfer of (material or immaterial) resources.
Unlike in the past, the data have become more robust, quickly produced, and accumulated to allow a different dimension on increasing productivity. Through real‐time data production and acquisition from various sensors, much more detailed and micro forecasting and control are possible, allowing precise management. For example, there is a bullwhip effect in SCM (supply chain management). Incorrectly assessing the supply of raw materials results in a greater error in production output forecasting at the next step of the supply chain, resulting in an increased gap between actual sales amounts.