Search results for "Applied Graph Data Science"
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Large-Scale Graph Processing Using Apache Giraph
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.
R 2,198.00
Graph Data Processing with Cypher
Get acquainted with Cypher in a guided manner quickly and learn how to query the graph databases with efficient and performant queriesKey FeaturesWork with Cypher syntax and semantics while building graph traversal queriesGet up and running with advanced Cypher concepts like List, Maps, OPTIONAL MATCHMaster best practices in writing effective queries leveraging data modeling and patternsBook DescriptionWhile it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way.This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You'll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users.After having worked through this book, you'll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.What you will learnWrite Cypher queries from basic to advanced levelMap the source data to the graph data model in an iterative fashionLoad the data into a graph using LOAD CSV, APOC, and client driversMap the business questions to graph queries effectivelyIdentify query performance issues and fix themExtend capabilities of Cypher using APOC utilitiesWork with graph visualization tools like Bloom and BrowserWho this book is forThis book is targeted at Database Administrator, Database Developers, Graph Database Developers, and Graph Database Architects. This book will also help someone migrate from a DBA role to a graph data engineer or data scientistIf you are working with graph databases and need to learn Cypher, or are a basic Cypher developer who wants to get better at data modeling and tuning queries to build performant Cypher queries, then this is the book for you.
R 1,368.00
Angewandte Data Science
Die Anwendungen der Disziplin Data Science erweitern und wandeln sich stetig. In diesem Buch geben Insider aus Praxis, Wissenschaft und Lehre detailliert die Ergebnisse ihrer Data-Science-Projekte, Methodenwissen sowie Knowhow zu Vorgehensweisen und Prozessmodellen an den Leser weiter. Dabei wird ein weit gespannter Querschnitt an konkreten Anwendungen beschrieben, erklärt und illustriert: von der Nutzung generativer KI-Systeme über quantitative Textanalyse, Predictive Policing, Erklärbarkeit von Machine-Learning-Modellen, experimentelle Datenanalyse in der Spektroskopie bis hin zu Datenvisualisierung, Strukturgleichungsmodellen und Varianzanalyse.Das Buch richtet sich an jeden, der sowohl am konkreten Einsatz von Datenwissenschaft, Statistik, Maschinellem Lernen und Künstlicher Intelligenz als auch am zugehörigen theoretischen Hintergrund interessiert ist. Praktikern, Studierenden und Lehrenden dürfte es von besonderem Nutzen sein: eine Vielzahl an Abbildungen, Diagrammen und Illustrationen ergänzen die reichhaltigen Textinformationen; Links zu Webseiten und Webapplikationen verweisen auf online verfügbare weitere Informationsquellen und Data-Science-Werkzeuge.
R 1,465.00
R 1,985.00
Data Science in Context
Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
R 1,774.24
