Download Big Data Analysis: New Algorithms for a New Society by Nathalie Japkowicz, Jerzy Stefanowski PDF

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.

Show description

Read or Download Big Data Analysis: New Algorithms for a New Society PDF

Similar intelligence & semantics books

Learning Bayesian Networks

During this first variation booklet, tools are mentioned for doing inference in Bayesian networks and inference diagrams. hundreds of thousands of examples and difficulties permit readers to know the data. the various themes mentioned contain Pearl's message passing set of rules, Parameter studying: 2 possible choices, Parameter studying r possible choices, Bayesian constitution studying, and Constraint-Based studying.

Computer Algebra: Symbolic and Algebraic Computation

This hole. In 16 survey articles crucial theoretical effects, algorithms and software program equipment of machine algebra are lined, including systematic references to literature. furthermore, a few new effects are offered. hence the amount will be a worthwhile resource for acquiring a primary impact of machine algebra, in addition to for getting ready a working laptop or computer algebra path or for complementary analyzing.

Neural networks: algorithms, applications, and programming techniques

Freeman and Skapura supply a realistic creation to synthetic neural structures (ANS). The authors survey the commonest neural-network architectures and exhibit how neural networks can be utilized to resolve genuine medical and engineering difficulties and describe methodologies for simulating neural-network architectures on conventional electronic computing platforms

Additional resources for Big Data Analysis: New Algorithms for a New Society

Sample text

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.

Download PDF sample

Rated 4.88 of 5 – based on 28 votes