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Large Scale Graph Processing Using Apache Giraph


Large Scale Graph Processing Using Apache Giraph
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Large Scale Graph Processing Using Apache Giraph


Large Scale Graph Processing Using Apache Giraph
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Author : Sherif Sakr
language : en
Publisher: Springer
Release Date : 2017-01-05

Large Scale Graph Processing Using Apache Giraph written by Sherif Sakr and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-05 with Computers categories.


This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system’s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.



Practical Graph Analytics With Apache Giraph


Practical Graph Analytics With Apache Giraph
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Author : Roman Shaposhnik
language : en
Publisher: Apress
Release Date : 2015-11-19

Practical Graph Analytics With Apache Giraph written by Roman Shaposhnik and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-19 with Computers categories.


Practical Graph Analytics with Apache Giraph helps you build data mining and machine learning applications using the Apache Foundation’s Giraph framework for graph processing. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected data points. Graphs arise in a wealth of data scenarios and describe the connections that are naturally formed in both digital and real worlds. Examples of such connections abound in online social networks such as Facebook and Twitter, among users who rate movies from services like Netflix and Amazon Prime, and are useful even in the context of biological networks for scientific research. Whether in the context of business or science, viewing data as connected adds value by increasing the amount of information available to be drawn from that data and put to use in generating new revenue or scientific opportunities. Apache Giraph offers a simple yet flexible programming model targeted to graph algorithms and designed to scale easily to accommodate massive amounts of data. Originally developed at Yahoo!, Giraph is now a top top-level project at the Apache Foundation, and it enlists contributors from companies such as Facebook, LinkedIn, and Twitter. Practical Graph Analytics with Apache Giraph brings the power of Apache Giraph to you, showing how to harness the power of graph processing for your own data by building sophisticated graph analytics applications using the very same framework that is relied upon by some of the largest players in the industry today.



Giraph In Action


Giraph In Action
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Author : Claudio Martella
language : en
Publisher:
Release Date : 2015-04-07

Giraph In Action written by Claudio Martella and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-07 with categories.


Graph data structures are nothing more than representations of the relationship between entities. Although graph data tends to be intuitively understandable, graph algorithms must be extremely powerful and scalable to manage the nearly-incalculable potential relationships within large data sets. To efficiently process graph data, an equally powerful graph processing framework like Apache Giraph is essential. Apache Giraph supplies many algorithms needed to draw conclusions from graph data, but can also be used to design custom graph algorithms. Whether trying to identify patterns in social data, optimize the traffic on a network, or any set of highly-connected data, Giraph has the tools that allow users to focus on the meaning of data instead of the chore of processing it. Giraph in Action is a comprehensive guide that teaches the application of the Apache Giraph programming model to real-world graph data examples. It starts by showing how to mine graph data using the most straightforward algorithms. Then, it dives into the Giraph architecture and the main APIs as readers discover how to model and process more complex scenarios. Along the way, it offers techniques for handling data from disparate sources, swapping data in and out of memory, and running Giraph in the cloud. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



Apache Spark Graph Processing


Apache Spark Graph Processing
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Author : Rindra Ramamonjison
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-10

Apache Spark Graph Processing written by Rindra Ramamonjison 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 2015-09-10 with Computers categories.


Build, process and analyze large-scale graph data effectively with Spark About This Book Find solutions for every stage of data processing from loading and transforming graph data to Improve the scalability of your graphs with a variety of real-world applications with complete Scala code. A concise guide to processing large-scale networks with Apache Spark. Who This Book Is For This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed. What You Will Learn Write, build and deploy Spark applications with the Scala Build Tool. Build and analyze large-scale network datasets Analyze and transform graphs using RDD and graph-specific operations Implement new custom graph operations tailored to specific needs. Develop iterative and efficient graph algorithms using message aggregation and Pregel abstraction Extract subgraphs and use it to discover common clusters Analyze graph data and solve various data science problems using real-world datasets. In Detail Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. Apache Spark GraphX API combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. This book will teach the user to do graphical programming in Apache Spark, apart from an explanation of the entire process of graphical data analysis. You will journey through the creation of graphs, its uses, its exploration and analysis and finally will also cover the conversion of graph elements into graph structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark graphs using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform graph elements and graph structures. Furthermore, this book also teaches how to create custom graph operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering graphs, implementing graph-parallel iterative algorithms and learning methods from graph data. Style and approach A step-by-step guide that will walk you through the key ideas and techniques for processing big graph data at scale, with practical examples that will ensure an overall understanding of the concepts of Spark.



Large Scale Graph Analysis System Algorithm And Optimization


Large Scale Graph Analysis System Algorithm And Optimization
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Author : Yingxia Shao
language : en
Publisher: Springer Nature
Release Date : 2020-07-01

Large Scale Graph Analysis System Algorithm And Optimization written by Yingxia Shao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.


This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.



Encyclopedia Of Big Data Technologies


Encyclopedia Of Big Data Technologies
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Author : Sherif Sakr
language : en
Publisher: Springer
Release Date : 2019-03-01

Encyclopedia Of Big Data Technologies written by Sherif Sakr and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-01 with Computers categories.


The Encyclopedia of Big Data Technologies provides researchers, educators, students and industry professionals with a comprehensive authority over the most relevant Big Data Technology concepts. With over 300 articles written by worldwide subject matter experts from both industry and academia, the encyclopedia covers topics such as big data storage systems, NoSQL database, cloud computing, distributed systems, data processing, data management, machine learning and social technologies, data science. Each peer-reviewed, highly structured entry provides the reader with basic terminology, subject overviews, key research results, application examples, future directions, cross references and a bibliography. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals. In addition, the distinguished, international editorial board of the encyclopedia consists of well-respected scholars, each developing topics based upon their expertise.



Resource Management For Big Data Platforms


Resource Management For Big Data Platforms
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Author : Florin Pop
language : en
Publisher: Springer
Release Date : 2016-10-27

Resource Management For Big Data Platforms written by Florin Pop and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-27 with Computers categories.


Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.



Contemporary Issues In Communication Cloud And Big Data Analytics


Contemporary Issues In Communication Cloud And Big Data Analytics
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Author : Hiren Kumar Deva Sarma
language : en
Publisher: Springer Nature
Release Date : 2021-11-30

Contemporary Issues In Communication Cloud And Big Data Analytics written by Hiren Kumar Deva Sarma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-30 with Technology & Engineering categories.


This book presents the outcomes of the First International Conference on Communication, Cloud, and Big Data (CCB) held on December 18–19, 2020, at Sikkim Manipal Institute of Technology, Majitar, Sikkim, India. This book contains research papers and articles in the latest topics related to the fields like communication networks, cloud computing, big data analytics, and on various computing techniques. Research papers addressing security issues in above-mentioned areas are also included in the book. The research papers and articles discuss latest issues in the above-mentioned topics. The book is very much helpful and useful for the researchers, engineers, practitioners, research students, and interested readers.



Enabling Blockchain Technology For Secure Networking And Communications


Enabling Blockchain Technology For Secure Networking And Communications
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Author : Ben Mnaouer, Adel
language : en
Publisher: IGI Global
Release Date : 2021-06-11

Enabling Blockchain Technology For Secure Networking And Communications written by Ben Mnaouer, Adel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Computers categories.


In recent years, the surge of blockchain technology has been rising due to is proven reliability in ensuring secure and effective transactions, even between untrusted parties. Its application is broad and covers public and private domains varying from traditional communication networks to more modern networks like the internet of things and the internet of energy crossing fog and edge computing, among others. As technology matures and its standard use cases are established, there is a need to gather recent research that can shed light on several aspects and facts on the use of blockchain technology in different fields of interest. Enabling Blockchain Technology for Secure Networking and Communications consolidates the recent research initiatives directed towards exploiting the advantages of blockchain technology for benefiting several areas of applications that vary from security and robustness to scalability and privacy-preserving and more. The chapters explore the current applications of blockchain for networking and communications, the future potentials of blockchain technology, and some not-yet-prospected areas of research and its application. This book is ideal for practitioners, stakeholders, researchers, academicians, and students interested in the concepts of blockchain technology and the potential and pitfalls of its application in different utilization domains.



Applications Of Big Data Analytics


Applications Of Big Data Analytics
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Author : Mohammed M. Alani
language : en
Publisher: Springer
Release Date : 2018-07-23

Applications Of Big Data Analytics written by Mohammed M. Alani and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-23 with Computers categories.


This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.