Graph Data Management

George Fletcher is an Associate Professor and Chair of the Database Group at Eindhoven University of Technology. He obtained his PhD from Indiana University Bloomington in 2007. His research interests span data-intensive systems, including query language design and engineering, foundations of databases, and data integration. His current focus is on data engineering challenges in the management of massive graphs such as social networks and linked open data.
Jan Hidders is an Associate Professor at the Vrije Universiteit Brussel. He got his PhD in 2001 at Eindhoven University of Technology, and has subsequently worked at the University of Antwerp as a postdoctoral researcher and as an assistant professor at TUDelft. He has worked on topics such a database indexing, workflow languages, query optimization and data integration. His current interests are in languages and platforms for large scale distributed data processing and in particular for graph processing.
Josep Lluís Larriba-Pey is an Associate Professor at the Universitat Politècnica de Catalunya. He has been working on Graph management for the last 10 years leading DAMA-UPC, a research and Technology Development group at Universitat Politècnica de Catalunya. In addition, based on the technologies DAMA-UPC, Josep has founded Sparsity Technologies a spin out company that provides technologies based on graph management, and in particular the high performance graph database Sparksee.

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management.  Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains.
The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions.
The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

Presents a comprehensive overview of fundamental issues and recent advances in graph data management

Introduction.- Basics of Graph Data Management.- Large Scale Graph Processing Systems.- Motif Discovery in Large Graphs.- Applications of Flexible Querying to Graphs.- Graph Visualization.- Graph Management Benchmarking.- Parallel Processing of Graphs.- Graph Mining.- Connectivity Queries on Complex Networks.