Download or read book Software Foundations for Data Interoperability and Large Scale Graph Data Analytics written by Lu Qin and published by Springer Nature. This book was released on 2020-11-05 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the 4th International Workshop on Software Foundations for Data Interoperability, SFDI 2020, and 2nd International Workshop on Large Scale Graph Data Analytics, LSGDA 2020, held in Conjunction with VLDB 2020, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 full papers and 4 short papers were thoroughly reviewed and selected from 38 submissions. The volme presents original research and application papers on the development of novel graph analytics models, scalable graph analytics techniques and systems, data integration, and data exchange.

Download or read book Software Foundations for Data Interoperability written by George Fletcher and published by Springer Nature. This book was released on 2022-01-19 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers presented at the 5th International Workshop on Software Foundations for Data Interoperability, SFDI 2021, held in Copenhagen, Denmark, in August 2021. The 4 full papers and one short paper were thorougly reviewed and selected from 8 submissions. They present discussions in research and development in software foundations for data interoperability as well as the applications in real-world systems such as data markets.

Download or read book Foundations of Data Intensive Applications written by Supun Kamburugamuve and published by John Wiley & Sons. This book was released on 2021-08-11 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems

Download or read book Practical Graph Analytics with Apache Giraph written by Roman Shaposhnik and published by Apress. This book was released on 2015-11-19 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Large Scale Graph Processing Using Apache Giraph written by Sherif Sakr and published by Springer. This book was released on 2017-01-05 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook of Big Data Technologies written by Albert Y. Zomaya and published by Springer. This book was released on 2017-02-25 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.

Download or read book Massive Graph Analytics written by David A. Bader and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Expertise in massive scale graph analytics is key for solving real-world grand challenges from health to sustainability to detecting insider threats, cyber defense, and more. Massive Graph Analytics provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. The book will be beneficial to students, researchers and practitioners, in academia, national laboratories, and industry, who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive scale graph analytics"--

Download or read book Distributed Graph Analytics written by Unnikrishnan Cheramangalath and published by Springer Nature. This book was released on 2020-04-17 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.

Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Download or read book Large scale Graph Analysis System Algorithm and Optimization written by Yingxia Shao and published by Springer Nature. This book was released on 2020-07-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Massive scale Processing of Record oriented and Graph Data written by Semih Salihoglu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Many data-driven applications perform computations on large volumes of data that do not fit on a single computer. These applications typically must use parallel shared-nothing distributed software systems to perform their computations. This thesis addresses challenges in large-scale distributed data processing with a particular focus on two primary areas: (i) theoretical foundations for understanding the costs of distribution; and (ii) processing large-scale graph data. The first part of this thesis presents a theoretical framework for the MapReduce system, to analyze the cost of distribution for different problems domains, and for evaluating the ``goodness'' of different algorithms. We identify a fundamental tradeoff between the parallelism and communication costs of algorithms. We first study the setting when computations are constrained to a single round of MapReduce. In this setting, we capture the cost of distributing a problem by deriving a lower-bound curve on the communication cost of any algorithm that solves the problem for different parallelism levels. We derive lower-bound curves for several problems, and prove that existing or new one-round algorithms solving these problems are optimal, i.e., incur the minimum possible communication cost for different parallelism levels. We then show that by allowing multiple rounds of MapReduce computations, we can solve problems more efficiently than any possible one-round algorithm. The second part of this thesis addresses challenges in systems for processing large-scale graph data, with the goal of making graph computation more efficient and easier to program and debug. We focus on systems that are modeled after Google's Pregel framework for large-scale distributed graph processing. We begin by describing an open-source version of Pregel we developed, called GPS (for Graph Processing System). We then describe new static and dynamic schemes for partitioning graphs across machines, and we present experimental results on the performance effects of different partitioning schemes. Next, we describe a set of algorithmic optimizations that address commonly-appearing inefficiencies in algorithms programmed on Pregel-like systems. Because it can be very difficult to debug programs in Pregel-like systems, we developed a new replay-style debugger called Graft. In addition, we defined and implemented a set of high-level parallelizable graph primitives, called HelP (for High-level Primitives), as an alternative to programming graph algorithms using the low-level vertex-centric functions of existing systems. HelP primitives capture several commonly appearing operations in large-scale graph computations. We motivate and describe Graft and HelP using real-world applications and algorithms.

Download or read book Distributed Graph Analytics written by Unnikrishnan Cheramangalath and published by . This book was released on 2020 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concept s.

Download or read book Systems for Big Graph Analytics written by Da Yan and published by Springer. This book was released on 2017-05-31 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc. Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features.

Download or read book Data Analytics on Graphs written by Ljubisa Stankovic and published by . This book was released on 2020-12-22 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments. This book will be a useful friend and a helpful companion to all involved in data gathering and analysis.

Download or read book Model Management and Analytics for Large Scale Systems written by Bedir Tekinerdogan and published by Academic Press. This book was released on 2019-09-14 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. - Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics - Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics - Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer. This book was released on 2020-07-16 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features: Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description: Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What You Will Learn: Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Become well-versed with extracting data from social networks, financial transaction systems, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for: This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance. The book will also be useful for data scientists and machine learning developers who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.