Distributed Computing In Big Data Analytics
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| Author | : Sourav Mazumder |
| Publisher | : Springer |
| Total Pages | : 162 |
| Release | : 2017-08-29 |
| Genre | : Computers |
| ISBN | : 3319598341 |
Download Distributed Computing in Big Data Analytics Book in PDF, Epub and Kindle
Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.
| Author | : Benjamin Bengfort |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 288 |
| Release | : 2016-06 |
| Genre | : Computers |
| ISBN | : 1491913762 |
Download Data Analytics with Hadoop Book in PDF, Epub and Kindle
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib
| Author | : K.G. Srinivasa |
| Publisher | : Springer |
| Total Pages | : 310 |
| Release | : 2015-02-09 |
| Genre | : Computers |
| ISBN | : 3319134973 |
Download Guide to High Performance Distributed Computing Book in PDF, Epub and Kindle
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Features: describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing; presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution; Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding; Provides detailed case studies on approaches to clustering, data classification and regression analysis; Explains the process of creating a working recommender system using Scalding and Spark.
| Author | : Song Guo |
| Publisher | : Cambridge University Press |
| Total Pages | : 231 |
| Release | : 2022-02-10 |
| Genre | : Computers |
| ISBN | : 1108832377 |
Download Edge Learning for Distributed Big Data Analytics Book in PDF, Epub and Kindle
Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential reading for experienced researchers and developers, or for those who are just entering the field.
| Author | : Stéphane Devismes |
| Publisher | : Springer Nature |
| Total Pages | : 395 |
| Release | : 2024-01-03 |
| Genre | : Technology & Engineering |
| ISBN | : 3031505832 |
Download Distributed Computing and Intelligent Technology Book in PDF, Epub and Kindle
This book constitutes the refereed proceedings of the 20th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2024, which was held in Bhubaneswar, India, during January 17–20, 2024. The 24 full papers presented in this volume were carefully reviewed and selected from 116 submissions. The papers are organized in the following topical sections: Distributed Computing (DC) and Intelligent Technology (IT). The DC track solicits original research papers contributing to the foundations and applications of distributed computing, whereas the IT track solicits original research papers contributing to the foundations and applications of Intelligent Technology.
| Author | : Zaigham Mahmood |
| Publisher | : Springer |
| Total Pages | : 332 |
| Release | : 2016-07-05 |
| Genre | : Business & Economics |
| ISBN | : 3319318616 |
Download Data Science and Big Data Computing Book in PDF, Epub and Kindle
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
| Author | : Rajkumar Buyya |
| Publisher | : Springer |
| Total Pages | : 310 |
| Release | : 2014-09-02 |
| Genre | : Technology & Engineering |
| ISBN | : 3319112279 |
Download Intelligent Distributed Computing Book in PDF, Epub and Kindle
This book contains a selection of refereed and revised papers of the Intelligent Distributed Computing Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers selected for this Track cover several Distributed Computing and related topics including Peer-to-Peer Networks, Cloud Computing, Mobile Clouds, Wireless Sensor Networks, and their applications.
| Author | : Marcello Trovati |
| Publisher | : Springer |
| Total Pages | : 178 |
| Release | : 2016-01-12 |
| Genre | : Computers |
| ISBN | : 3319253131 |
Download Big-Data Analytics and Cloud Computing Book in PDF, Epub and Kindle
This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.
| Author | : Chittaranjan Hota |
| Publisher | : Springer |
| Total Pages | : 586 |
| Release | : 2013-01-11 |
| Genre | : Computers |
| ISBN | : 3642360718 |
Download Distributed Computing and Internet Technology Book in PDF, Epub and Kindle
This book constitutes the refereed proceedings of the 9th International Conference on Distributed Computing and Internet Technology, ICDCIT 2013, held in Bhubaneswar, India, in February 2013. The 40 full papers presented together with 5 invited talks in this volume were carefully reviewed and selected from 164 submissions. The papers cover various research aspects in distributed computing, internet technology, computer networks, and machine learning.
| Author | : M. Mittal |
| Publisher | : IOS Press |
| Total Pages | : 618 |
| Release | : 2018-01-31 |
| Genre | : Computers |
| ISBN | : 1614998140 |
Download Data Intensive Computing Applications for Big Data Book in PDF, Epub and Kindle
The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.
