Distributed Computing In Big Data Analytics
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Distributed Computing In Big Data Analytics
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Author : Sourav Mazumder
language : en
Publisher: Springer
Release Date : 2017-08-29
Distributed Computing In Big Data Analytics written by Sourav Mazumder and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-29 with Computers categories.
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.
Data Science And Big Data Computing
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Author : Zaigham Mahmood
language : en
Publisher: Springer
Release Date : 2016-07-05
Data Science And Big Data Computing written by Zaigham Mahmood and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-05 with Business & Economics categories.
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.
Practical Real Time Data Processing And Analytics
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Author : Shilpi Saxena
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-28
Practical Real Time Data Processing And Analytics written by Shilpi Saxena 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 2017-09-28 with Computers categories.
A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner. Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams.
Data Intensive Computing Applications For Big Data
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Author : Mamta Mittal
language : en
Publisher: SAGE Publications Limited
Release Date : 2018-01-15
Data Intensive Computing Applications For Big Data written by Mamta Mittal and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-15 with Computers categories.
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.
Intelligent Distributed Computing Xii
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Author : Javier Del Ser
language : en
Publisher: Springer
Release Date : 2018-09-14
Intelligent Distributed Computing Xii written by Javier Del Ser and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-14 with Computers categories.
This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
Mastering Distributed Computing
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Distributed Computing written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Uncover the Art of Seamless Distributed Computing with "Mastering Distributed Computing" In the dynamic realm of modern computing, the ability to harness the power of distributed systems is paramount. "Mastering Distributed Computing" is your definitive guide to mastering the art of seamlessly orchestrating distributed resources for optimal performance and scalability. Whether you're an experienced software engineer or a newcomer to the world of distributed computing, this book equips you with the knowledge and skills needed to navigate the intricacies of distributed systems. About the Book: "Mastering Distributed Computing" takes you on an enlightening journey through the intricacies of distributed computing, from foundational concepts to advanced techniques. From distributed architectures to consensus algorithms, this book covers it all. Each chapter is meticulously designed to provide both a deep understanding of the concepts and practical applications in real-world scenarios. Key Features: · Foundational Principles: Build a strong foundation by understanding the core principles of distributed systems, including scalability, fault tolerance, and data consistency. · Distributed Architectures: Explore a range of distributed architectures, including client-server, peer-to-peer, microservices, and serverless, understanding their strengths and applications. · Consistency and Replication: Dive into the complexities of data consistency and replication strategies, including eventual consistency, strong consistency, and distributed databases. · Distributed Algorithms: Master fundamental distributed algorithms, such as leader election, distributed locking, and distributed transactions, for coordinating actions across nodes. · Scaling Strategies: Discover strategies for scaling distributed systems horizontally and vertically, ensuring optimal performance as workloads grow. · Fault Tolerance: Understand mechanisms for building fault-tolerant systems, including redundancy, replication, and failure detection and recovery. · Real-World Use Cases: Gain insights from real-world examples spanning industries, from finance and e-commerce to social media and beyond. · Cloud and Edge Computing: Explore the role of distributed computing in cloud environments and edge computing scenarios, and their impact on modern applications. · Security and Privacy: Explore best practices for securing distributed systems, data encryption, access control, and compliance. Who This Book Is For: "Mastering Distributed Computing" is designed for software engineers, architects, developers, and anyone passionate about effective distributed system design. Whether you're seeking to enhance your skills or embark on a journey toward becoming a distributed computing expert, this book provides the insights and tools to navigate the complexities of distributed systems. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Intelligent Distributed Computing Xiii
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Author : Igor Kotenko
language : en
Publisher: Springer Nature
Release Date : 2019-10-01
Intelligent Distributed Computing Xiii written by Igor Kotenko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Technology & Engineering categories.
This book gathers research contributions on recent advances in intelligent and distributed computing. A major focus is placed on new techniques and applications for several highlydemanded research directions: Internet of Things, Cloud Computing and Big Data, Data Mining and Machine Learning, Multi-agent and Service-Based Distributed Systems, Distributed Algorithms and Optimization, Modeling Operational Processes, Social Network Analysis and Inappropriate Content Counteraction, Cyber-Physical Security and Safety, Intelligent Distributed Decision Support Systems, Intelligent Human-Machine Interfaces, VisualAnalytics and others. The book represents the peer-reviewed proceedings of the 13thInternational Symposium on Intelligent Distributed Computing (IDC 2019), which was held in St. Petersburg, Russia, from October 7 to 9, 2019.
Distributed Computing And Artificial Intelligence 15th International Conference
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Author : Fernando De La Prieta
language : en
Publisher: Springer
Release Date : 2018-07-04
Distributed Computing And Artificial Intelligence 15th International Conference written by Fernando De La Prieta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-04 with Technology & Engineering categories.
The 15th International Symposium on Distributed Computing and Artificial Intelligence 2018 (DCAI 2018) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the University of Castilla-La Mancha, the Osaka Institute of Technology and the University of Salamanca. The present edition was held in Toledo, Spain, from 20th – 22nd June, 2018.
Data Science
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Author : Dr.L.Ramesh
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-11-25
Data Science written by Dr.L.Ramesh and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-25 with Computers categories.
Dr.L.Ramesh, Assistant Professor, Department of Computer Applications (UG), School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. Mrs.S.Seema, Research Scholar, Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. Mrs.R.Jeevitha, Research Scholar, Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. Mrs.Janani.S, Research Scholar, Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India.
Machine Learning In Forensic Evidence Examination
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Author : Niha Ansari
language : en
Publisher: CRC Press
Release Date : 2025-09-15
Machine Learning In Forensic Evidence Examination written by Niha Ansari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-15 with Social Science categories.
The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science. Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges. Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies.