Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

Sue E. Kase; Michelle Vanni; Joanne A. Knight; Yu Su; Xifeng Yan

doi:10.1117/12.2228380

12 May 2016 Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

Sue E. Kase, Michelle Vanni, Joanne A. Knight, Yu Su, Xifeng Yan

Proceedings Volume 9851, Next-Generation Analyst IV; 985104 (2016) https://doi.org/10.1117/12.2228380
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Within operational environments decisions must be made quickly based on the information available. Identifying an appropriate knowledge base and accurately formulating a search query are critical tasks for decision-making effectiveness in dynamic situations. The spreading of graph data management tools to access large graph databases is a rapidly emerging research area of potential benefit to the intelligence community. A graph representation provides a natural way of modeling data in a wide variety of domains. Graph structures use nodes, edges, and properties to represent and store data. This research investigates the advantages of information search by graph query initiated by the analyst and interactively refined within the contextual dimensions of the answer space toward a solution. The paper introduces SLQ, a user-friendly graph querying system enabling the visual formulation of schemaless and structureless graph queries. SLQ is demonstrated with an intelligence analyst information search scenario focused on identifying individuals responsible for manufacturing a mosquito-hosted deadly virus. The scenario highlights the interactive construction of graph queries without prior training in complex query languages or graph databases, intuitive navigation through the problem space, and visualization of results in graphical format.

Citation Download Citation

Sue E. Kase, Michelle Vanni, Joanne A. Knight, Yu Su, and Xifeng Yan "Visual graph query formulation and exploration: a new perspective on information retrieval at the edge", Proc. SPIE 9851, Next-Generation Analyst IV, 985104 (12 May 2016); https://doi.org/10.1117/12.2228380

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KEYWORDS

Analytical research

Databases

Visualization

Chemical engineering

Human-machine interfaces

Data modeling

Chemical analysis

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