By Nathalie Japkowicz, Jerzy Stefanowski
This edited quantity is dedicated to important information research from a desktop studying viewpoint as provided through probably the most eminent researchers during this quarter.
It demonstrates that giant facts research opens up new study difficulties that have been both by no means thought of sooner than, or have been basically thought of inside of a restricted variety. as well as offering methodological discussions at the rules of mining huge facts and the adaptation among conventional statistical information research and more recent computing frameworks, this ebook provides lately constructed algorithms affecting such parts as enterprise, monetary forecasting, human mobility, the web of items, info networks, bioinformatics, scientific structures and existence technology. It explores, via a few particular examples, how the research of massive information research has developed and the way it has all started and may probably proceed to impact society. whereas the advantages introduced upon through huge info research are underlined, the publication additionally discusses the various warnings which were issued in regards to the capability hazards of massive info research besides its pitfalls and challenges.
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Additional resources for Big Data Analysis: New Algorithms for a New Society
7. Issues to consider when using Big Data Analysis in the Business field Dealing with data uncertainties in the Financial Domain Dealing with Capacity issues in the Insurance Domain New issues in Big Data Analysis emanating from the Internet of Things The mining of complex Information Networks in the Telecommunication Sector Issues to consider when using Big Data Analysis for DNA sequencing High-dimensionality in Life Science problems We now give a brief summary of each of these chapters in turn, and explain how they fit in the framework we have created.
From big data to big data mining: challenges, issues and opportunities. In: Hong, B, et al. ) DASFAA Workshops, Springer LNCS 7827, pp. 1–15 (2013) 16. : Big data: a survey. Mobile New Appl. 19, 171–209 (2014) 17. : An approach to evaluate data trustworthiness based on data provenance. In: Proceedings of the 5th VLDB Workshop on Secure Data Management, pp. 82– 98 (2008) 18. : Provenance and scientific workflows: challenges and opportunities. In: Proceedings of the SIGMOD’08 (2008) 19. : Ethics of Big Data.
Feature Selection for Life Science Problems Chapter “Discovering networks of interdependent features in high-dimensional problems” by Michał Draminski, Michał J. D¸abrowski, Klev Diamanti, Jacek Koronacki, and Jan Komorowski presents a new methodology for selecting features and discovering their interactions in extremely high dimensional problems such as those encountered in the field of Life Sciences. Using their previously designed Monte-Carlo Feature Selection algorithm to rank the features, they then proceed to construct a directed graph that models the interactions between these features and the strengths of their interdependencies.