The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (Early Releas e)
By 作者:Denise Gosnell, Matthias Broecheler (Author)
pages 页数: 420 pages
Publisher Finelybook 出版社: O'Reilly Media; 1 edition (April 7, 2020)
Language 语言: English
The Book Description robot was collected from Amazon and arranged by Finelybook
With Early Release Finelybook 出版日期 ebooks, you get books in their earliest form—the authors' raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.
This book will enable you to apply graph thinking to solve complex problems. If you want to learn how to build architectures for extracting value for your domain’s complex problems, then this book is for you.
You’ll learn how to think about your data as a graph, and how to determine if graph technology is right for your application. The book describes techniques for scalable, real-time, and multimodel architectures that solve complex problems, and shows how companies are successfully applying graph thinking in distributed production environments.
Authors Denise Koessler Gosnell and Matthias Broecheler also introduce the Graph Schema Language, a set of terminology and visual illustrations to normalize how graph practitioners communicate conceptual graph models, graph schema, and graph database design.
About the Author
Dr. Denise Gosnell’s passion for examining, applying, and evangelizing the applications of graph data was ignited during her apprenticeship under Dr. Teresa Haynes and Dr. Debra Knisley during her first NSF Fellowship. This group’s work was one of the earliest applications of neural networks and graph theoretic structure in predictive computational biology. Since then, Dr. Gosnell has built, published, patented, and spoke on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals.
Currently, Dr. Gosnell is with DataStax where she aspires to build upon her experiences as a data scientist and graph architect. Prior to her role with DataStax, she built software solutions for and spoke at over a dozen conferences on permissioned blockchains, machine learning applications of graph analytics, and data science within the healthcare industry.
Dr. Matthias Broecheler is a technologist and entrepreneur with substantial research anddevelopment experience who is focused on disruptive software technologies and understanding complex systems. Dr. Broecheler’s is known as an industry expert in graph databases, relational machine learning, and big data analysis in general. He is a practitioner of lean methodologies and experimentation to drive continuous improvement. Dr. Broecheler is the inventor of the Titan graph database and a founder of Aurelius.
2.The Graph Schema Language:a Tool for Learning Graph Thinking
3.Your First Application:Customer 360
4.Exploring Neighborhoods of Data
5.The Internals of Graph Data in Apache Cassandra