High Performance Data Mining and Big Data Analytics

High Performance Data Mining and Big Data Analytics

Author: Khosrow Hassibi

Publisher: Createspace Independent Pub

Published: 2014-10-07

Total Pages: 294

ISBN-13: 9781495301070

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The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late '80s and the early '90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data mining science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual's transaction spending patterns have been used since early '90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. While a decade ago, the masses did not know how their detailed data were being used by corporations for decision making, today they are fully aware of that fact. Many people, especially the millennial generation, voluntarily provide detailed information about themselves. Today people know that any mouse click they generate, any comment they write, any transaction they perform, and any location they go to, may be captured and analyzed for some business purpose. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology's true relevance. I wrote this book to provide an objective view of analytics trends today. I have written it in complete independence, and solely as a personal passion. As a result, the views expressed in this book are those of the author and do not necessarily represent the views of, and should not be attributed to, any vendor or employer.Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data. High-performance computing architectures have been devised to address the need for handling big data, not only from a transaction processing standpoint but also from a tactical and strategic analytics viewpoint. The success of big data analytics in large web companies has created a rush toward understanding the impact of new big data technologies in classic analytics environments that already employ a multitude of legacy analytics technologies. There is a wide variety of readings about big data, high-performance computing for analytics, massively parallel processing (MPP) databases, Hadoop and its ecosystem, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and big data analytics. However, none of these readings provides an overview of these topics in a single document. The objective of this book is to provide a historical and comprehensive view of the recent trend toward high-performance computing technologies, especially as it relates to big data analytics and high-performance data mining. The book also emphasizes the impact of big data on requiring a rethinking of every aspect of the analytics life cycle, from data management, to data mining and analysis, to deployment.As a result of interactions with different stakeholders in classic organizations, I realized there was a need for a more holistic view of big data analytics' impact across classic organizations, and also the impact of high-performance computing techniques on legacy data mining. Whether you are an executive, manager, data scientist, analyst, sales or IT staff, the holistic and broad overview provided in the book will help in grasping the important topics in big data analytics and its potential impact in your organizations.


Book Synopsis High Performance Data Mining and Big Data Analytics by : Khosrow Hassibi

Download or read book High Performance Data Mining and Big Data Analytics written by Khosrow Hassibi and published by Createspace Independent Pub. This book was released on 2014-10-07 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late '80s and the early '90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data mining science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual's transaction spending patterns have been used since early '90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. While a decade ago, the masses did not know how their detailed data were being used by corporations for decision making, today they are fully aware of that fact. Many people, especially the millennial generation, voluntarily provide detailed information about themselves. Today people know that any mouse click they generate, any comment they write, any transaction they perform, and any location they go to, may be captured and analyzed for some business purpose. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology's true relevance. I wrote this book to provide an objective view of analytics trends today. I have written it in complete independence, and solely as a personal passion. As a result, the views expressed in this book are those of the author and do not necessarily represent the views of, and should not be attributed to, any vendor or employer.Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data. High-performance computing architectures have been devised to address the need for handling big data, not only from a transaction processing standpoint but also from a tactical and strategic analytics viewpoint. The success of big data analytics in large web companies has created a rush toward understanding the impact of new big data technologies in classic analytics environments that already employ a multitude of legacy analytics technologies. There is a wide variety of readings about big data, high-performance computing for analytics, massively parallel processing (MPP) databases, Hadoop and its ecosystem, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and big data analytics. However, none of these readings provides an overview of these topics in a single document. The objective of this book is to provide a historical and comprehensive view of the recent trend toward high-performance computing technologies, especially as it relates to big data analytics and high-performance data mining. The book also emphasizes the impact of big data on requiring a rethinking of every aspect of the analytics life cycle, from data management, to data mining and analysis, to deployment.As a result of interactions with different stakeholders in classic organizations, I realized there was a need for a more holistic view of big data analytics' impact across classic organizations, and also the impact of high-performance computing techniques on legacy data mining. Whether you are an executive, manager, data scientist, analyst, sales or IT staff, the holistic and broad overview provided in the book will help in grasping the important topics in big data analytics and its potential impact in your organizations.

High-Performance Big-Data Analytics

High-Performance Big-Data Analytics

Author: Pethuru Raj

Publisher: Springer

Published: 2015-10-16

Total Pages: 443

ISBN-13: 331920744X

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This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.


Book Synopsis High-Performance Big-Data Analytics by : Pethuru Raj

Download or read book High-Performance Big-Data Analytics written by Pethuru Raj and published by Springer. This book was released on 2015-10-16 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.

Big Data, Data Mining, and Machine Learning

Big Data, Data Mining, and Machine Learning

Author: Jared Dean

Publisher: John Wiley & Sons

Published: 2014-05-27

Total Pages: 293

ISBN-13: 1118618041

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With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.


Book Synopsis Big Data, Data Mining, and Machine Learning by : Jared Dean

Download or read book Big Data, Data Mining, and Machine Learning written by Jared Dean and published by John Wiley & Sons. This book was released on 2014-05-27 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Big Data and Visual Analytics

Big Data and Visual Analytics

Author: Sang C. Suh

Publisher: Springer

Published: 2018-01-15

Total Pages: 263

ISBN-13: 331963917X

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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.


Book Synopsis Big Data and Visual Analytics by : Sang C. Suh

Download or read book Big Data and Visual Analytics written by Sang C. Suh and published by Springer. This book was released on 2018-01-15 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

High Performance Computing for Big Data

High Performance Computing for Big Data

Author: Chao Wang

Publisher: CRC Press

Published: 2017-10-16

Total Pages: 430

ISBN-13: 1351651579

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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 book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.


Book Synopsis High Performance Computing for Big Data by : Chao Wang

Download or read book High Performance Computing for Big Data written by Chao Wang and published by CRC Press. This book was released on 2017-10-16 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.

Data Analytics

Data Analytics

Author: Herbert Jones

Publisher: Createspace Independent Publishing Platform

Published: 2018-09-19

Total Pages: 152

ISBN-13: 9781727481013

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If you want to learn about data analytics and data mining then keep reading... 2 comprehensive manuscripts in 1 book Data Analytics: An Essential Beginner's Guide To Data Mining, Data Collection, Big Data Analytics For Business, And Business Intelligence Concepts Data Mining: The Data Mining Guide for Beginners, Including Applications for Business, Data Mining Techniques, Concepts, and More With this book, not only will you understand all the internal nitty-gritties about data analytics and data mining, you will also understand why data analytics and data mining is changing the business arena. You'll realize that the high-performance analytics will enable you to do stuff that you never thought about before probably because the volumes of data were just too big (among other reasons) and so much more. Here are just some of the topics that are discussed in the first part of this book: Overview Of Data Analytics: What Is Data Analytics (And Big Data Analytics)? Data Analytics And Business Intelligence Data Analysis And Data Analytics Data Mining Data Collection Types Of Data Analytics The Process: The Lifecycle Of Big Data Analytics Behavioral Analytics: Using Big Data Analytics To Find Hidden Customer Behavior Patterns Further Pattern Discovery In Advanced Analytics: Machine Learning And Much, Much More In part 2 of this book, you will learn the following: Model creation How to prepare your data How to clean your data Data Mining Similarity and distances of data The effect of data distribution Association pattern mining What is cluster analysis? What is an outlier in data mining? How to deal with outliers in data mining Methods of identifying outliers in data Applications of data mining in the business industry So if you are serious about becoming an expert in data analytics and data mining, start with this book by clicking "add to cart"!


Book Synopsis Data Analytics by : Herbert Jones

Download or read book Data Analytics written by Herbert Jones and published by Createspace Independent Publishing Platform. This book was released on 2018-09-19 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to learn about data analytics and data mining then keep reading... 2 comprehensive manuscripts in 1 book Data Analytics: An Essential Beginner's Guide To Data Mining, Data Collection, Big Data Analytics For Business, And Business Intelligence Concepts Data Mining: The Data Mining Guide for Beginners, Including Applications for Business, Data Mining Techniques, Concepts, and More With this book, not only will you understand all the internal nitty-gritties about data analytics and data mining, you will also understand why data analytics and data mining is changing the business arena. You'll realize that the high-performance analytics will enable you to do stuff that you never thought about before probably because the volumes of data were just too big (among other reasons) and so much more. Here are just some of the topics that are discussed in the first part of this book: Overview Of Data Analytics: What Is Data Analytics (And Big Data Analytics)? Data Analytics And Business Intelligence Data Analysis And Data Analytics Data Mining Data Collection Types Of Data Analytics The Process: The Lifecycle Of Big Data Analytics Behavioral Analytics: Using Big Data Analytics To Find Hidden Customer Behavior Patterns Further Pattern Discovery In Advanced Analytics: Machine Learning And Much, Much More In part 2 of this book, you will learn the following: Model creation How to prepare your data How to clean your data Data Mining Similarity and distances of data The effect of data distribution Association pattern mining What is cluster analysis? What is an outlier in data mining? How to deal with outliers in data mining Methods of identifying outliers in data Applications of data mining in the business industry So if you are serious about becoming an expert in data analytics and data mining, start with this book by clicking "add to cart"!

Data Driven Decision Making using Analytics

Data Driven Decision Making using Analytics

Author: Parul Gandhi

Publisher: CRC Press

Published: 2021-12-21

Total Pages: 135

ISBN-13: 1000506495

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This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.


Book Synopsis Data Driven Decision Making using Analytics by : Parul Gandhi

Download or read book Data Driven Decision Making using Analytics written by Parul Gandhi and published by CRC Press. This book was released on 2021-12-21 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.

Big Data Systems

Big Data Systems

Author: Jawwad Ahmad Shamsi

Publisher: CRC Press

Published: 2021-05-11

Total Pages: 370

ISBN-13: 0429531575

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Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples. Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples for thorough understanding. Contains programming examples for hands on development. Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning. Exemplifies widely used big data technologies such as Hadoop and Spark. Includes discussion on case studies and open issues. Provides end of chapter questions for enhanced learning.


Book Synopsis Big Data Systems by : Jawwad Ahmad Shamsi

Download or read book Big Data Systems written by Jawwad Ahmad Shamsi and published by CRC Press. This book was released on 2021-05-11 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples. Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples for thorough understanding. Contains programming examples for hands on development. Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning. Exemplifies widely used big data technologies such as Hadoop and Spark. Includes discussion on case studies and open issues. Provides end of chapter questions for enhanced learning.

Big Data Analytics for Sensor-Network Collected Intelligence

Big Data Analytics for Sensor-Network Collected Intelligence

Author: Hui-Huang Hsu

Publisher: Morgan Kaufmann

Published: 2017-02-02

Total Pages: 328

ISBN-13: 012809625X

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Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Contains contributions from noted scholars in computer science and electrical engineering from around the globe Provides a broad overview of recent developments in sensor collected intelligence Edited by a team comprised of leading thinkers in big data analytics


Book Synopsis Big Data Analytics for Sensor-Network Collected Intelligence by : Hui-Huang Hsu

Download or read book Big Data Analytics for Sensor-Network Collected Intelligence written by Hui-Huang Hsu and published by Morgan Kaufmann. This book was released on 2017-02-02 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Contains contributions from noted scholars in computer science and electrical engineering from around the globe Provides a broad overview of recent developments in sensor collected intelligence Edited by a team comprised of leading thinkers in big data analytics

Big Data and High Performance Computing

Big Data and High Performance Computing

Author: L. Grandinetti

Publisher: IOS Press

Published: 2015-10-20

Total Pages: 168

ISBN-13: 1614995834

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Big Data has been much in the news in recent years, and the advantages conferred by the collection and analysis of large datasets in fields such as marketing, medicine and finance have led to claims that almost any real world problem could be solved if sufficient data were available. This is of course a very simplistic view, and the usefulness of collecting, processing and storing large datasets must always be seen in terms of the communication, processing and storage capabilities of the computing platforms available. This book presents papers from the International Research Workshop, Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2014. The papers selected for publication here discuss fundamental aspects of the definition of Big Data, as well as considerations from practice where complex datasets are collected, processed and stored. The concepts, problems, methodologies and solutions presented are of much more general applicability than may be suggested by the particular application areas considered. As a result the book will be of interest to all those whose work involves the processing of very large data sets, exascale computing and the emerging fields of data science


Book Synopsis Big Data and High Performance Computing by : L. Grandinetti

Download or read book Big Data and High Performance Computing written by L. Grandinetti and published by IOS Press. This book was released on 2015-10-20 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data has been much in the news in recent years, and the advantages conferred by the collection and analysis of large datasets in fields such as marketing, medicine and finance have led to claims that almost any real world problem could be solved if sufficient data were available. This is of course a very simplistic view, and the usefulness of collecting, processing and storing large datasets must always be seen in terms of the communication, processing and storage capabilities of the computing platforms available. This book presents papers from the International Research Workshop, Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2014. The papers selected for publication here discuss fundamental aspects of the definition of Big Data, as well as considerations from practice where complex datasets are collected, processed and stored. The concepts, problems, methodologies and solutions presented are of much more general applicability than may be suggested by the particular application areas considered. As a result the book will be of interest to all those whose work involves the processing of very large data sets, exascale computing and the emerging fields of data science