Comparison Study Between Mapreduce Mr And Parallel Data Management Systems Dbms In Large Scale Data Analysis

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Comparison Study Between MapReduce (MR) and Parallel Data Management Systems (DBMs) in Large Scale Data Analysis

Author : Miriam Lawrence Mchome
Publisher :
ISBN 13 :
Total Pages : 44 pages
Book Rating : 4.:/5 (768 download)

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Book Synopsis Comparison Study Between MapReduce (MR) and Parallel Data Management Systems (DBMs) in Large Scale Data Analysis by : Miriam Lawrence Mchome

Download or read book Comparison Study Between MapReduce (MR) and Parallel Data Management Systems (DBMs) in Large Scale Data Analysis written by Miriam Lawrence Mchome and published by . This book was released on 2011 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the quantity of structured and unstructured data increases, data processing experts have turned to systems that analyze data using many computers in parallel. This study looks at two systems designed for these needs: MapReduce and parallel databases. In the MapReduce programming model, users express their problem in terms of a map function and a reduce function. Parallel databases organize data as a system of tables representing entities and relationships between them. Previous comparison studies have focused on performance, concluding that these two systems are complimentary. Parallel databases scored high on performance and MapReduce scored high on flexibility in handling unstructured data. Both systems offer a querying language: Pig Latin for MapReduce systems and SQL for parallel databases. This study compares the operations, query structure and support for user defined functions in these languages. The findings offer data processing experts insights into how data organization and querying structure affects data analysis.

Massively Parallel Databases and MapReduce Systems

Author : Herodotos Herodotou
Publisher :
ISBN 13 : 9781601987518
Total Pages : 120 pages
Book Rating : 4.9/5 (875 download)

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Book Synopsis Massively Parallel Databases and MapReduce Systems by : Herodotos Herodotou

Download or read book Massively Parallel Databases and MapReduce Systems written by Herodotos Herodotou and published by . This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:

User-Defined Tensor Data Analysis

Author : Bin Dong
Publisher : Springer Nature
ISBN 13 : 3030707504
Total Pages : 111 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis User-Defined Tensor Data Analysis by : Bin Dong

Download or read book User-Defined Tensor Data Analysis written by Bin Dong and published by Springer Nature. This book was released on 2021-09-29 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: The SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.

International Conference on Communication, Computing and Electronics Systems

Author : V. Bindhu
Publisher : Springer Nature
ISBN 13 : 9811526125
Total Pages : 742 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis International Conference on Communication, Computing and Electronics Systems by : V. Bindhu

Download or read book International Conference on Communication, Computing and Electronics Systems written by V. Bindhu and published by Springer Nature. This book was released on 2020-03-04 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high impact papers presented at the International Conference on Communication, Computing and Electronics Systems 2019, held at the PPG Institute of Technology, Coimbatore, India, on 15-16 November, 2019. Discussing recent trends in cloud computing, mobile computing, and advancements of electronics systems, the book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.

Large Scale and Big Data

Author : Sherif Sakr
Publisher : CRC Press
ISBN 13 : 1466581506
Total Pages : 640 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Large Scale and Big Data by : Sherif Sakr

Download or read book Large Scale and Big Data written by Sherif Sakr and published by CRC Press. This book was released on 2014-06-25 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

Information Management and Machine Intelligence

Author : Dinesh Goyal
Publisher : Springer Nature
ISBN 13 : 9811549362
Total Pages : 658 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Information Management and Machine Intelligence by : Dinesh Goyal

Download or read book Information Management and Machine Intelligence written by Dinesh Goyal and published by Springer Nature. This book was released on 2020-09-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected papers presented at the International Conference on Information Management and Machine Intelligence (ICIMMI 2019), held at the Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India, on December 14–15, 2019. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

Big Data Analytics

Author : Erik Steven Paulson
Publisher :
ISBN 13 :
Total Pages : 108 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Big Data Analytics by : Erik Steven Paulson

Download or read book Big Data Analytics written by Erik Steven Paulson and published by . This book was released on 2018 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data is now pervasive. This has driven a critical need to develop novel methods to store and process data at large scale, as well as to develop new applications to use and make sense of this data. This dissertation makes two contributions toward addressing this need. First, we study methods for large-scale data analysis. In particular, we compare the popular MapReduce model to parallel relational database management systems, and empirically analyze their strengths and weaknesses. We evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a collection of benchmarks that we have run on an open-source version of MR as well as on two parallel DBMSs. For each benchmark, we measure each system's performance for various degrees of parallelism on a cluster of 100 shared-nothing nodes. Our results reveal some interesting trade-offs. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures. In the second contribution, we examine how Big Data scaling methods can be used to build a scalable and flexible cloud-based entity matching applications, and what lessons can be learned for future development of similar applications. Entity matching (EM) finds disparate data instances that refer to the same real-world entity. EM has been long studied and is crucial to many fields, and will become even more so in the age of Big Data. However, it is still very difficult for domain scientists to use EM systems, especially at scale. In response, we have developed CloudMatcher, a cloud/crowd service for EM. CloudMatcher aims to be a fast, easy- to-use, scalable, and highly available EM service on the Web. As far as we can tell, no such application has been developed for EM in the data management research community. We describe CloudMatcher's development and deployment, providing a detailed analysis of its performance over several representative datasets and in several scale-up experiments, and discussing lessons learned. Taken together, our contributions in this dissertation advance the topic of Big Data analytics, for both aspects of methods and applications.

Mining Very Large Databases with Parallel Processing

Author : Alex A. Freitas
Publisher : Springer Science & Business Media
ISBN 13 : 1461555213
Total Pages : 211 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Mining Very Large Databases with Parallel Processing by : Alex A. Freitas

Download or read book Mining Very Large Databases with Parallel Processing written by Alex A. Freitas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.

Conquering Big Data with High Performance Computing

Author : Ritu Arora
Publisher : Springer
ISBN 13 : 3319337424
Total Pages : 328 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Conquering Big Data with High Performance Computing by : Ritu Arora

Download or read book Conquering Big Data with High Performance Computing written by Ritu Arora and published by Springer. This book was released on 2016-09-16 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.

Big Data 2.0 Processing Systems

Author : Sherif Sakr
Publisher : Springer Nature
ISBN 13 : 3030441873
Total Pages : 145 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Big Data 2.0 Processing Systems by : Sherif Sakr

Download or read book Big Data 2.0 Processing Systems written by Sherif Sakr and published by Springer Nature. This book was released on 2020-07-09 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

Techniques and Environments for Big Data Analysis

Author : B. S.P. Mishra
Publisher : Springer
ISBN 13 : 3319275208
Total Pages : 199 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Techniques and Environments for Big Data Analysis by : B. S.P. Mishra

Download or read book Techniques and Environments for Big Data Analysis written by B. S.P. Mishra and published by Springer. This book was released on 2016-02-05 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.

Big Data Optimization: Recent Developments and Challenges

Author : Ali Emrouznejad
Publisher : Springer
ISBN 13 : 3319302655
Total Pages : 492 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Big Data Optimization: Recent Developments and Challenges by : Ali Emrouznejad

Download or read book Big Data Optimization: Recent Developments and Challenges written by Ali Emrouznejad and published by Springer. This book was released on 2016-05-26 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.

Fundamentals of Big Data Analytics - An Introduction with Java, Hadoop, Pig, and Hive

Author : Mr.Rama Bhadra Rao Maddu
Publisher : PND Publishers
ISBN 13 : 8194949165
Total Pages : 189 pages
Book Rating : 4.1/5 (949 download)

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Book Synopsis Fundamentals of Big Data Analytics - An Introduction with Java, Hadoop, Pig, and Hive by : Mr.Rama Bhadra Rao Maddu

Download or read book Fundamentals of Big Data Analytics - An Introduction with Java, Hadoop, Pig, and Hive written by Mr.Rama Bhadra Rao Maddu and published by PND Publishers. This book was released on with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book title "Fundamentals of Big Data Analytics - An Introduction with Java, Hadoop, Pig, and Hive" accurately reflects the comprehensive coverage of essential data structures in Java, as well as the detailed exploration of big data technologies like Hadoop, Pig, and Hive. It provides a solid foundation in both programming with Java and handling large-scale data using popular big data tools. This title effectively captures the essence and scope of the content presented in the chapters you outlined.

Web Data Management

Author : Serge Abiteboul
Publisher : Cambridge University Press
ISBN 13 : 113950505X
Total Pages : 451 pages
Book Rating : 4.1/5 (395 download)

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Book Synopsis Web Data Management by : Serge Abiteboul

Download or read book Web Data Management written by Serge Abiteboul and published by Cambridge University Press. This book was released on 2011-11-28 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses.

Handbook of Research on Big Data Clustering and Machine Learning

Author : Garcia Marquez, Fausto Pedro
Publisher : IGI Global
ISBN 13 : 1799801071
Total Pages : 478 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Big Data Clustering and Machine Learning by : Garcia Marquez, Fausto Pedro

Download or read book Handbook of Research on Big Data Clustering and Machine Learning written by Garcia Marquez, Fausto Pedro and published by IGI Global. This book was released on 2019-10-04 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

Big Data and Hadoop

Author : Mayank Bhusan
Publisher : BPB Publications
ISBN 13 : 9386551993
Total Pages : 333 pages
Book Rating : 4.3/5 (865 download)

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Book Synopsis Big Data and Hadoop by : Mayank Bhusan

Download or read book Big Data and Hadoop written by Mayank Bhusan and published by BPB Publications. This book was released on 2018-06-02 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book contains the latest trend in IT industry 'BigData and Hadoop'. It explains how big is 'Big Data' and why everybody is trying to implement this into their IT project.It includes research work on various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends.Big Data and Hadoop have taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience and would be a reference to help readers.KEY FEATURES Overview Of Big Data, Basics of Hadoop, Hadoop Distributed File System, HBase, MapReduce, HIVE: The Dataware House Of Hadoop, PIG: The Higher Level Programming Environment, SQOOP: Importing Data From Heterogeneous Sources, Flume, Ozzie, Zookeeper & Big Data Stream Mining, Chapter-wise Questions & Previous Years Questions

Data Warehouse Performance Comparing Relational Database Management Systems and the Hadoop-based NoSQL Database System

Author : Nazar Al-Wattar
Publisher :
ISBN 13 :
Total Pages : 156 pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Data Warehouse Performance Comparing Relational Database Management Systems and the Hadoop-based NoSQL Database System by : Nazar Al-Wattar

Download or read book Data Warehouse Performance Comparing Relational Database Management Systems and the Hadoop-based NoSQL Database System written by Nazar Al-Wattar and published by . This book was released on 2020 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: "One of the biggest problems that many companies face nowadays is dealing with the huge volumes of data that they generate daily. In the data-driven world all data needs to be stored, organized and analyzed to get the required information that will help the administration to make the right decision to support the next step of the company. Big Data and Business Intelligence have become very popular terms in the business field, where Big Data highlights the tools that are used to manage the huge volume of data. One of the Big Data tools is the Data Warehouse, which is used to manipulate the massive amount of data, while the Business Intelligence (BI) focuses on how we can analyze information from the huge volumes of data that support companies in decision making. In this thesis, we will compare the implementation of the DW concepts using the Relational Database Management Systems (RDBMS), specifically, SQL Server DB over the Hadoop system, and then analyze the resource (CPU and RAM) consumption. I prove that using the Hadoop system speeds up the process of manipulating these huge volumes of data with very low cost, based on the nature of the Hadoop system that is efficient in processing all kinds of structured, semi-structured, unstructured or raw data with minimum cost and high efficiency in manipulating and storing massive amounts of data."--Abstract.