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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. An Introduction to Graph Data Management

Graph data management concerns the research and development of powerful technologies for storing, processing and analyzing large volumes of graph data. This chapter presents an overview about the foundations and systems for graph data management. Specifically, we present a historical overview of the area, studied graph database models, characterized essential graph-oriented queries, reviewed graph query languages, and explore the features of current graph data management systems (i.e. graph databases and graph-processing frameworks).
Renzo Angles, Claudio Gutierrez

Chapter 2. Graph Visualization

Graphs provide a versatile model for data from a large variety of application domains, for example, software engineering, telecommunication, and biology. Understanding the information that is represented by the graph is crucial for scientists and engineers to understand critical issues in these domains. Graph visualization is the process of creating a drawing of a graph so that a human can understand the graph. However, the depth of understanding depends on the quality of the graph representation. Good visualization can facilitate efficient visual analysis of the data to detect patterns and trends. Important aspects of the development of graph drawing methods are the efficiency and accuracy of the algorithms, and the quality of the resulting picture. In this chapter, we discuss the geometric properties of good graph visualizations as node-link diagrams, and describe methods for constructing good layouts of graphs.
Peter Eades, Karsten Klein

Chapter 3. gLabTrie: A Data Structure for Motif Discovery with Constraints

Motif discovery is the problem of finding subgraphs of a network that appear surprisingly often. Each such subgraph may indicate a small-scale interaction feature in applications ranging from a genomic interaction network, a significant relationship involving rock musicians, or any other application that can be represented as a network. We look at the problem of constrained search for motifs based on labels (e.g. gene ontology, musician type to continue our example from above). This chapter presents a brief review of the state of the art in motif finding and then extends the gTrie data structure from Ribeiro and Silva (Data Min Knowl Discov 28(2):337–377, 2014b) to support labels. Experiments validate the usefulness of our structure for small subgraphs, showing that we recoup the cost of the index after only a handful of queries.
Misael Mongioví, Giovanni Micale, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti, Dennis Shasha

Chapter 4. Applications of Flexible Querying to Graph Data

Graph data models provide flexibility and extensibility, which makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information-seeking requirements. There is a need, therefore, for flexible querying systems over graph data that can automatically make changes to the user’s query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations.
Alexandra Poulovassilis

Chapter 5. Parallel Processing of Graphs

Graphs play an indispensable role in a wide range of application domains. Graph processing at scale, however, is facing challenges at all levels, ranging from system architectures to programming models. In this chapter, we review the challenges of parallel processing of large graphs, representative graph processing systems, general principles of designing large graph processing systems, and various graph computation paradigms. Graph processing covers a wide range of topics and graphs can be represented in different forms. Different graph representations lead to different computation paradigms and system architectures. From the perspective of graph representation, this chapter also briefly introduces a few alternative forms of graph representation besides adjacency list.
Bin Shao, Yatao Li

Chapter 6. A Survey of Benchmarks for Graph-Processing Systems

Benchmarking is a process that informs the public about the capabilities of systems-under-test, focuses on expected and unexpected system-bottlenecks, and promises to facilitate system tuning and new systems designs. In this chapter, we survey benchmarking approaches for graph-processing systems. First, we describe the main features of a benchmark for graph-processing systems. Then, we systematically survey across these features a diverse set of benchmarks for RDF databases, benchmarks for graph databases, benchmarks for parallel and distributed graph-processing systems, and data-only benchmarks. We trace in our survey not only the important benchmarks, but also their innovative approaches and how their core ideas evolved from previous benchmarking approaches. Last, we identify ongoing and future research directions for benchmarking initiatives.
Angela Bonifati, George Fletcher, Jan Hidders, Alexandru Iosup
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