Have Green - A Visual Analytics Framework for Large Semantic Graphs

Author

Wong, Pak Chung ; Chin, George, Jr. ; Foote, Harlan ; Mackey, Patrick ; Thomas, Jim

Author_Institution

Pacific Northwest Nat. Lab., Richland, WA

fYear

2006

fDate

Oct. 31 2006-Nov. 2 2006

Firstpage

67

Lastpage

74

Abstract

A semantic graph is a network of heterogeneous nodes and links annotated with a domain ontology. In intelligence analysis, investigators use semantic graphs to organize concepts and relationships as graph nodes and links in hopes of discovering key trends, patterns, and insights. However, as new information continues to arrive from a multitude of sources, the size and complexity of the semantic graphs will soon overwhelm an investigator´s cognitive capacity to carry out significant analyses. We introduce a powerful visual analytics framework designed to enhance investigators´ natural analytical capabilities to comprehend and analyze large semantic graphs. The paper describes the overall framework design, presents major development accomplishments to date, and discusses future directions of a new visual analytics system known as Have Green

Keywords

data visualisation; semantic networks; Have Green; graph visualization; information analytics; information visualization; network visualization; semantic graph; visual analytics framework; Computer displays; Fuses; Information analysis; Laboratories; Ontologies; Pattern analysis; Performance analysis; Software systems; Visual analytics; Visualization; 1.6.9 [Visualization] - Information Visualization, Visualization Systems and Software, Visualization Techniques Methodologies; Graph and Network Visualization; Information Analytics; Information Visualization; Visual Analytics;

fLanguage

English

Publisher

ieee

Conference_Titel

Visual Analytics Science And Technology, 2006 IEEE Symposium On

Conference_Location

Baltimore, MD

Print_ISBN

1-4244-0591-2

Electronic_ISBN

1-4244-0592-0

Type

conf

DOI

10.1109/VAST.2006.261432

Filename

4035749