Design efficient and flexible databases by optimizing the power of Neo4j About This BookModel your data as a graph using Neo4j to design databases with minimum hassleDiscover new patterns using graphs and solve problems that are difficult to solve using any other databaseStep-by-step guide to designing a graph model with pitfalls and design choicesWho This Book Is ForIf you are a developer who wants to understand the fundamentals of modeling data in Neo4j and how it can be used to model full-fledged applications, then this book is for you. Some understanding of domain modeling may be advantageous but is not essential.What You Will LearnTranslate a problem domain from a whiteboard to your databaseMake design decisions based on the nature of data and how it is going to be usedUse Cypher to create and query dataEvolve your database in stagesOptimize the performance of your application with data designDesign paradigms to ensure flexibility, ease of querying, and performanceMove from an existing model to a new model without losing consistencyIn DetailNeo4j is a graph database that allows you to model your data as a graph and find solutions to complex real-world problems that are difficult to solve using any other type of database.This book is designed to help you understand the intricacies of modeling a graph for any domain.The book starts with an example of a graph problem and then introduces you to modeling non-graph problems using Neo4j. Concepts such as the evolution of your database, chains, access control, and recommendations are addressed, along with examples and are modeled in a graph. Throughout the book, you will discover design choices and trade-offs, and understand how and when to use them. By the end of the book, you will be able to effectively use Neo4j to model your database for efficiency and flexibility.
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- Guo H, Scriney M and Liu K (2024). An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems, Information Systems Frontiers, 26:1, (277-300), Online publication date: 1-Feb-2024.
- Lagraa S, Husák M, Seba H, Vuppala S, State R and Ouedraogo M (2024). A review on graph-based approaches for network security monitoring and botnet detection, International Journal of Information Security, 23:1, (119-140), Online publication date: 1-Feb-2024.
- Yuan G, Lu J, Yan Z and Wu S (2023). A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data, ACM Computing Surveys, 55:10, (1-38), Online publication date: 31-Oct-2023.
- Tanveer A, Sharma C, Sinha R and Kuo M (2023). Tracing security requirements in industrial control systems using graph databases, Software and Systems Modeling (SoSyM), 22:3, (851-870), Online publication date: 1-Jun-2023.
- Sun Y, Gui W, Han C, Zhang Y and Zhang S AIServiceX: A Knowledge Graph-Based Intelligent Question-Answering System for Personal Services Services – SERVICES 2020, (85-92)
- Jingbin W and Jing L Graph Data Retrieval Algorithm for Knowledge Fragmentation Web Information Systems and Applications, (443-448)
- Vágner A Store and Visualize EER in Neo4j Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, (1-6)
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