Computational and Statistical Methods for Analysing Big Data with Applications

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate

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  • Author : Shen Liu
  • Publisher : Academic Press
  • Pages : 206 pages
  • ISBN : 0081006519
  • Rating : 5/5 from 4 reviews
CLICK HERE TO GET THIS BOOKComputational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications
  • Author : Shen Liu,James Mcgree,Zongyuan Ge,Yang Xie
  • Publisher : Academic Press
  • Release : 20 November 2015
GET THIS BOOKComputational and Statistical Methods for Analysing Big Data with Applications

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an

Computational Methods for Data Analysis

Computational Methods for Data Analysis
  • Author : Yeliz Karaca,Carlo Cattani
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 17 December 2018
GET THIS BOOKComputational Methods for Data Analysis

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

Data Analysis and Applications 3

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  • Author : Andreas Makrides,Alex Karagrigoriou,Christos H. Skiadas
  • Publisher : John Wiley & Sons
  • Release : 05 May 2020
GET THIS BOOKData Analysis and Applications 3

Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and

Computational Intelligence for Big Data Analysis

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  • Publisher : Springer
  • Release : 21 April 2015
GET THIS BOOKComputational Intelligence for Big Data Analysis

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each

Systems Simulation and Modeling for Cloud Computing and Big Data Applications

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  • Author : Dinesh Peter,Steven L. Fernandes
  • Publisher : Academic Press
  • Release : 26 February 2020
GET THIS BOOKSystems Simulation and Modeling for Cloud Computing and Big Data Applications

Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced

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  • Author : Sergei V. Chekanov
  • Publisher : Springer
  • Release : 23 March 2016
GET THIS BOOKNumeric Computation and Statistical Data Analysis on the Java Platform

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a

Spatial Analysis with R

Spatial Analysis with R
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  • Publisher : CRC Press
  • Release : 01 September 2020
GET THIS BOOKSpatial Analysis with R

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based

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Big Data Analytics and Computing for Digital Forensic Investigations
  • Author : Suneeta Satpathy,Sachi Nandan Mohanty
  • Publisher : CRC Press
  • Release : 07 April 2020
GET THIS BOOKBig Data Analytics and Computing for Digital Forensic Investigations

Digital forensics has recently gained a notable development and become the most demanding area in today’s information security requirement. This book investigates the areas of digital forensics, digital investigation and data analysis procedures as they apply to computer fraud and cybercrime, with the main objective of describing a variety of digital crimes and retrieving potential digital evidence. Big Data Analytics and Computing for Digital Forensic Investigations gives a contemporary view on the problems of information security. It presents the

High Performance Computing for Big Data

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  • Author : Chao Wang
  • Publisher : CRC Press
  • Release : 16 October 2017
GET THIS BOOKHigh Performance Computing for Big Data

High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the

Combining Soft Computing and Statistical Methods in Data Analysis

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  • Author : Christian Borgelt,Gil González Rodríguez,Wolfgang Trutschnig,María Asunción Lubiano,María Angeles Gil,Przemyslaw Grzegorzewski,Olgierd Hryniewicz
  • Publisher : Springer Science & Business Media
  • Release : 12 October 2010
GET THIS BOOKCombining Soft Computing and Statistical Methods in Data Analysis

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical

Big Data and Social Science

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  • Author : Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane
  • Publisher : CRC Press
  • Release : 18 November 2020
GET THIS BOOKBig Data and Social Science

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify

Computational Learning Approaches to Data Analytics in Biomedical Applications

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  • Author : Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
  • Publisher : Academic Press
  • Release : 29 November 2019
GET THIS BOOKComputational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes

Spatial Analysis

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  • Author : Tonny J. Oyana,Florence Margai
  • Publisher : CRC Press
  • Release : 28 July 2015
GET THIS BOOKSpatial Analysis

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis—containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS—as well

Big Data and Social Science

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  • Author : Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane
  • Publisher : CRC Press
  • Release : 10 August 2016
GET THIS BOOKBig Data and Social Science

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of

Handbook of Big Data Analytics

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  • Author : Wolfgang Karl Härdle,Henry Horng-Shing Lu,Xiaotong Shen
  • Publisher : Springer
  • Release : 20 July 2018
GET THIS BOOKHandbook of Big Data Analytics

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have