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Querying graphs
Bonifati A., Fletcher G., Voigt H., Yakovets N., Morgan&Claypool Publishers, San Rafael, CA, 2018. 184 pp.  Type: Book (978-1-681734-30-9)
Date Reviewed: Nov 21 2019

Graph data management (GDM) has become an increasingly important discipline, both in academia and in industry. One reason is that graph data has been ubiquitously created, collected, and released for analysis and learning. Every day, massive amounts of graph data emerges from every aspect of our lives, including social networks, biology, citations, transportation networks, and so on. The challenges of managing graph data primarily rest in its commonly unstructured essence, particularly its lack of fundamental schema and metadata. This makes the research of GDM dramatically distinct from the research of relational data management. Also, the fundamental focus of GDM lies in the contents (or things), associations, and patterns of connecting among atomic graph vertices (or nodes). Nevertheless, there lacks a literature source covering the increasing gap in the research of GDM and its background elements. Fortunately, this book provides a comprehensive overview of the life of graph query and an up-to-date survey of the state-of-the-art research in GDM.

The authors rigorously used the property graph model (PGM) as the primary research language of this book, which is the predominant graph data model adopted by mainstream graph databases. Starting with PGM in chapter 2, the authors present the full life cycle of graph queries, namely, graph query languages, data structures, indexes, query processing, and physical operators. In particular, chapter 3 introduces the property graph query language, which is the central topic of GDM. It mainly discusses the regular queries (RQ) and regular property graph queries (RPGQ) that consist of regular property graph logic (RPGLog) and regular property graph algebra (RPGA).

Chapter 4 presents the concept of graph constraints used to define an important class of graph metadata. The authors focus on graph functional dependencies (GFDs) and then introduce graph entity dependencies (GEDs) to define fundamental concepts such as keys and functional dependencies. It defines more complex dependencies such as graph denial constraints (GDCs) and graph-to-graph dependencies. Chapter 5 introduces the special graph specification problem, which aims to construct graph queries from labeled examples in a reverse engineering manner. This problem has been well studied in its complexity and has drawn growing interest toward this field.

Chapter 6 discusses the graph data structures and indexes covering the content of ternary relations, value compression, value indexing, pivoted table, and schema stabilization techniques. It introduces two important indexing subdisciplines, namely adjacency indexing and reachability indexing. Chapter 7 focuses on query processing in a graph database system. It gives an overview of a query pipeline, which is a standardized workflow for processing declarative queries. Chapter 8 introduces the design and implementation of physical query operators in graph database systems. Finally, Chapter 9 presents open problems and research challenges related to next-generation GDM.

The overall structure of this book is well organized for progressive understanding and study. The knowledge included is in depth, but mostly self contained. Nevertheless, readers should at least have a background in relational database systems before studying this book. Another good feature, each chapter ends with a comprehensive research summary and a vivid historical bibliography that provide rich background information and the current state of the research development.

The book’s primary audience includes developers and researchers focusing on graph databases and graph processing systems. It is also an excellent textbook for an advanced seminar or special topic course focusing on GDM.

Reviewer:  Feng Yu Review #: CR146792 (2003-0047)
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