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题名/责任者:

Human interaction with graphs : a visual querying perspective / Sourav S. Bhowmick, Byron Choi, Chengkai Li.

出版发行项:

[San Rafael, California] : Morgan and Claypool, 2018.

ISBN:

9781681733784 :

载体形态项:

xxii, 186 pages : illustrations ; 24 cm.

丛编说明:

Synthesis lectures on data management, 2153-5426 ; # 47

丛编统一题名:

Synthesis digital library of engineering and computer science.

丛编统一题名:

Synthesis lectures on data management, # 47.

个人责任者:

Bhowmick, Sourav S., author.

附加个人名称:

Choi, Byron.

附加个人名称:

Li, Chengkai.

论题主题:

Graph theory-Data processing.

论题主题:

Querying (Computer science)

论题主题:

Human-computer interaction.

中图法分类号:

TP391.41

一般附注:

Part of: Synthesis digital library of engineering and computer science.

书目附注:

Includes bibliographical references (pages 177-184)

内容附注:

1. Introduction -- 1.1 Interaction with graphs using queries -- 1.2 Graph query construction using visual interfaces -- 1.3 Integration of visual query interface and query engine -- 1.4 Overview of this book -- 1.5 Scope --

内容附注:

2. Background -- 2.1 Graph terminology -- 2.1.1 Subgraph isomorphism-related terminology -- 2.1.2 Types of graph collection -- 2.1.3 Frequent and infrequent features -- 2.2 Visual graph query interface -- 2.2.1 Structure of visual graph query interfaces -- 2.2.2 Visual graph query formulation -- 2.2.3 Query formulation-related terminology -- 2.3 Summary --

内容附注:

3. Guidance for visual query formulation -- 3.1 Overview of AutoG -- 3.2 Query composition -- 3.2.1 Definition -- 3.2.2 Query autocompletion modes -- 3.2.3 C-prime features -- 3.3 Autocompletion framework in AutoG -- 3.3.1 Query decomposition -- 3.3.2 Generation of candidate suggestions -- 3.3.3 Ranking candidate suggestions -- 3.4 Indexed autocompletion-AutoGI -- 3.4.1 Feature DAG (FDAG) index -- 3.4.2 Autocompletion by using FDAG -- 3.5 Performance study -- 3.5.1 Suggestion quality -- 3.5.2 Efficiency -- 3.6 Guidance for queries over large networks -- 3.7 Bibliographic notes -- 3.8 Conclusion --

内容附注:

4. Blending human interactions and graph query processing -- 4.1 Visual substructure search problem -- 4.2 A unified framework -- 4.2.1 The framework -- 4.2.2 Generality of the framework -- 4.2.3 An instantiation -- 4.3 Action-aware indexing -- 4.3.1 Key features of action-aware index -- 4.3.2 Action-aware frequent (A2F) index -- 4.3.3 Action-aware infrequent (A2 I) index -- 4.4 Spindle-shaped graph (SPIG) -- 4.4.1 Algorithm for SPIG construction -- 4.4.2 Analysis of SPIG construction -- 4.5 Substructure similarity search -- 4.5.1 Exact substructure candidates set generation -- 4.5.2 Similar substructure candidates set generation -- 4.5.3 Generation of approximate query results -- 4.6 Supporting query modification -- 4.7 Performance study -- 4.7.1 Experimental setup -- 4.7.2 Performance on real graph dataset -- 4.7.3 Performance on synthetic graph dataset -- 4.8 Bibliographic notes -- 4.9 Conclusions --

内容附注:

5. Blending interactions and query processing on large networks -- 5.1 Overview and contributions -- 5.1.1 Visual substructure search problem revisited -- 5.1.2 Overview -- 5.2 Decomposition of a large network -- 5.2.1 Graphlets and adjacent graphlets -- 5.2.2 Supergraphlets -- 5.3 Indexing frequent and infrequent fragments -- 5.3.1 Frequent and infrequent fragments -- 5.3.2 Fragment join -- 5.3.3 Generation of frequent fragments and SIFs -- 5.3.4 Index construction -- 5.4 Graphlet-based SPIG -- 5.4.1 Structure of G-SPIG -- 5.4.2 Algorithm -- 5.5 Blending visual subgraph query -- 5.5.1 Candidate data graphs generation -- 5.5.2 Generation of query results -- 5.6 Performance study -- 5.6.1 Experimental setup -- 5.6.2 System response time (SRT) -- 5.6.3 Index size -- 5.6.4 Prefetching time -- 5.6.5 Performance on a million-nodes network -- 5.7 Bibliographic notes -- 5.8 Conclusions --

内容附注:

6. Human interaction with query results -- 6.1 Results exploration for small- or medium-sized data graphs -- 6.1.1 Picasso -- 6.2 Results exploration on large networks -- 6.2.1 Region-based exploration -- 6.2.2 Exemplar-based exploration -- 6.2.3 Feature-based exploration -- 6.3 Bibliographic notes -- 6.4 Conclusions --

内容附注:

7. Simulation of visual subgraph query formulation -- 7.1 Overview of visual -- 7.2 Index-based generation of subgraph queries -- 7.3 Quantitative modeling of visual query formulation -- 7.3.1 Modeling query formulation time -- 7.3.2 Model extensibility -- 7.4 Simulation of visual subgraph query construction -- 7.4.1 Graph representation of query formulation -- 7.4.2 The visual algorithm -- 7.4.3 Finding minimal and maximal QFS -- 7.5 Performance study -- 7.5.1 Performance of test subgraph query generation -- 7.5.2 Performance of the query formulation model and visual -- 7.5.3 Application of visual -- 7.6 Bibliographic notes -- 7.7 Conclusions --

内容附注:

8. The road ahead -- 8.1 Summary -- 8.2 Future research -- Bibliography -- Authors' biographies.

摘要附注:

Interacting with graphs using queries has emerged as an important research problem for real-world applications that center on large graph data. Given the syntactic complexity of graph query languages (e.g., SPARQL, Cypher), visual graph query interfaces make it easy for nonprogrammers to query such graph data repositories. In this book, we present recent developments in the emerging area of visual graph querying paradigm that bridges traditional graph querying with human computer interaction (HCI) Specifically, we focus on techniques that emphasize deep integration between the visual graph query interface and the underlying graph query engine. We discuss various strategies and guidance for constructing graph queries visually, interleaving processing of graph queries and visual actions, visual exploration of graph query results, and automated performance study of visual graph querying frameworks. In addition, this book highlights open problems and new research directions. In summary, in this book, we review and summarize the research thus far into the integration of HCI and graph querying to facilitate user-friendly interaction with graph-structured data, giving researchers a snapshot of the current state of the art in this topic, and future research directions.

豆瓣简介：