Big Data And Visual Analytics
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Big Data And Visual Analytics
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Author : Sang C. Suh
language : en
Publisher: Springer
Release Date : 2018-01-15
Big Data And Visual Analytics written by Sang C. Suh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-15 with Computers categories.
This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.
Big Data With Sas Visual Analytics
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Author : Cesar Lopez Perez
language : en
Publisher: CreateSpace
Release Date : 2014-01
Big Data With Sas Visual Analytics written by Cesar Lopez Perez and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01 with Computers categories.
Is known as Big Data the treatment and analysis of large amounts of data, whose size makes it impossible to treat them with databases and conventional analytical tools. The proliferation of web pages, applications, image and video, social networks, mobile devices, apps, sensors and other modern devices capable of generating huge amounts of data have necessitated the development of Big Data Tools for analysis. We talk about an absolutely relevant environment for many aspects, from the analysis of phenomena, such as climate or seismographic data, to environments such as health, safety, or, of course, the business field. As for Big Data Tools, there is a growing development. Oracle uses Exadata for these purposes, SAS uses Visual Analytics and open-source Hadoop, a framework that is highly popular in this field that enables applications to work with huge repositories of data and thousands of nodes, inspired by Google tools like MapReduce and Google File System, or NoSQL, non-relational database systems needed to hold and process the enormous complexity of all data generated highlights, and in many cases do not follow the logic of the ACID (atomicity, consistency, isolation, durability) guarantees characteristic of conventional databases. This book delves into the treatment of the techniques of BIG DATA with SAS VISUAL ANALYTICS application
Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali
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Author : AUTHOR:1-Dr. Mehraj Ali Usman Ali, AUTHOR:2 -Dr. Shakeb Khan
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali written by AUTHOR:1-Dr. Mehraj Ali Usman Ali, AUTHOR:2 -Dr. Shakeb Khan and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE In an era dominated by technological advancements, the ability to extract meaningful insights from the ever-expanding volume of data has become a competitive advantage for organizations worldwide. Big Data, with its vast scope, provides companies with unprecedented opportunities to understand consumer behavior, optimize operations, and forecast future trends. Yet, despite its potential, raw data alone is insufficient; it needs to be processed, analyzed, and interpreted in a way that yields actionable insights. This is where Predictive Analytics comes into play. Predictive analytics is the practice of using historical data, machine learning algorithms, and statistical models to forecast future outcomes and trends. By leveraging Big Data, predictive analytics allows organizations to anticipate future behaviors, market shifts, and operational needs with remarkable accuracy. This predictive power is transforming industries, from retail and healthcare to finance and manufacturing, by providing businesses with tools to make data-driven decisions rather than relying solely on intuition or past experience. The goal of this book is to explore the intersection of Big Data and Predictive Analytics, providing readers with both theoretical insights and practical approaches to harnessing predictive models in Big Data environments. Throughout the chapters, we will cover the various types of predictive models, including regression analysis, time-series forecasting, decision trees, and neural networks, highlighting how these models can be applied to Big Data to solve real-world challenges. These methodologies are essential for applications ranging from demand forecasting and fraud detection to personalized marketing and healthcare diagnostics. Data preparation plays a pivotal role in predictive analytics, and this book will delve into the critical process of cleaning, transforming, and normalizing Big Data to ensure accurate and reliable predictions. Additionally, we will explore the implementation of machine learning algorithms, such as supervised and unsupervised learning, which form the backbone of many predictive models used in modern business applications. One of the core themes of this book is to demonstrate how predictive analytics is not just a tool for data scientists but a crucial component of decision support systems, helping organizations make informed choices across various departments, including marketing, operations, and finance. The book will also address the challenges that come with predictive analytics, such as data quality, overfitting, and model interpretability, providing solutions to these common obstacles. Through detailed case studies, particularly in the financial, retail, and healthcare sectors, this book highlights the transformative impact of predictive analytics in Big Data. By the end of this book, readers will not only gain an understanding of the core principles of predictive analytics but will also be equipped with the knowledge to apply these techniques in their own organizations to drive meaningful business outcomes. We hope this book serves as both an academic resource and a practical guide, empowering professionals, researchers, and students to fully leverage predictive analytics in the context of Big Data. Authors Dr. Mehraj Ali Usman Ali Dr. Shakeb Khan
2015 Big Data Visual Analytics Bdva
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Author : Ulrich Engelke
language : en
Publisher:
Release Date : 2015
2015 Big Data Visual Analytics Bdva written by Ulrich Engelke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Visual Analytics And Interactive Technologies Data Text And Web Mining Applications
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Author : Zhang, Qingyu
language : en
Publisher: IGI Global
Release Date : 2010-10-31
Visual Analytics And Interactive Technologies Data Text And Web Mining Applications written by Zhang, Qingyu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-31 with Computers categories.
"This book is a comprehensive reference on concepts, algorithms, theories, applications, software, and visualization of data mining, text mining, Web mining and computing/supercomputing, covering state-of-the-art of the theory and applications of mining"--
Big Data Visualization
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-02-28
Big Data Visualization written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-28 with Computers categories.
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI. Style and approach With the help of insightful real-world use cases, we'll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.
Predictive Intelligence Using Big Data And The Internet Of Things
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Author : Gupta, P.K.
language : en
Publisher: IGI Global
Release Date : 2018-12-28
Predictive Intelligence Using Big Data And The Internet Of Things written by Gupta, P.K. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-28 with Computers categories.
With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers.
Visual Analytics For Data Scientists
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Author : Natalia Andrienko
language : en
Publisher: Springer Nature
Release Date : 2020-08-30
Visual Analytics For Data Scientists written by Natalia Andrienko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-30 with Computers categories.
This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.
Big Data Technologies And Applications
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Author : Borko Furht
language : en
Publisher: Springer
Release Date : 2016-09-16
Big Data Technologies And Applications written by Borko Furht and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-16 with Computers categories.
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.
Advances In E Business Engineering For Ubiquitous Computing
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Author : Kuo-Ming Chao
language : en
Publisher: Springer Nature
Release Date : 2019-11-27
Advances In E Business Engineering For Ubiquitous Computing written by Kuo-Ming Chao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-27 with Technology & Engineering categories.
This book presents the latest trends in scientific methods and enabling technologies to advance e-business. It consists of selected high-quality papers from the 16th International Conference on E-Business Engineering (ICEBE 2019), held in Shanghai, China, on 11–13 October 2019. ICEBE is a leading international forum for researchers, engineers, and business specialists to exchange cutting-edge ideas, findings, and experiences in the field of e-business. The book covers a range of topics, including agents for e-business, big data for e-business, Internet of Things, mobile and autonomous computing, security/privacy/trust, service-oriented and cloud computing, software engineering, blockchain, and industry applications.