Big data concepts theories and applications pdf

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big data concepts theories and applications pdf

Big Data: Survey, Technologies, Opportunities, and Challenges

Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth continuous or discrete or time. Petrophysical data exhibits all the "7V" characteristics of big data, i. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
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Big Data Analytics - Big Data Explained - Big Data Tools & Trends - Big Data Training - Edureka

Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions f. Big Data Concepts, Theories, and Applications PDF · Security Theories and Practices for Big Data. Lei Xu, Weidong Shi.

Big Data in Psychology: Introduction to Special Issue

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By , 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase.

Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Skip to main content Skip to table of contents.

All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Big data is one of the hottest research topics in science and technology communities, and it possesses a great potential in every sector for our society, such as climate, economy, health, social science, and so on.

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Data with many cases rows offer greater statistical power , while data with higher complexity more attributes or columns may lead to a higher false discovery rate. Big data was originally associated with three key concepts: volume , variety , and velocity. Therefore, big data often includes data with sizes that exceed the capacity of traditional usual software to process within an acceptable time [4] and value. Current usage of the term big data tends to refer to the use of predictive analytics , user behavior analytics , or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Scientists encounter limitations in e-Science work, including meteorology , genomics , [8] connectomics , complex physics simulations, biology and environmental research.

The introduction to this special issue on psychological research involving big data summarizes the highlights of 10 articles that address a number of important and inspiring perspectives, issues, and applications. Four common themes that emerge in the articles with respect to psychological research conducted in the area of big data are mentioned, including: 1. The benefits of collaboration across disciplines, such as those in the social sciences, applied statistics, and computer science. Doing so assists in grounding big data research in sound theory and practice, as well as in affording effective data retrieval and analysis. Availability of large datasets on Facebook, Twitter, and other social media sites that provide a psychological window into the attitudes and behaviors of a broad spectrum of the population. Identifying, addressing, and being sensitive to ethical considerations when analyzing large datasets gained from public or private sources.



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