Visualizing the commonalities between hierarchically structured data queries

Chad Jones; Ryan Armstrong; Kwan-Liu Ma

Visualizing the commonalities between hierarchically structured data queries

Chad Jones, Ryan Armstrong, Kwan-Liu Ma

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper we present an information visualization approach for visualizing the intersection between multiple feature hierarchies. In cases where a dataset has multiple complex features of interest, especially ones that have been hierarchically clustered, it is often very difficult to grasp the commonalities between them. Understanding the overlap between features can help researchers discover correlations or hot spots in their data. Our method steps back from actual data features, simplifying their representation, and presenting them in a fashion which facilitates interpretation of their inter-feature relationships. We call this new representation a knowledge graph, and we show how it can be used to investigate the overlap of multiple features in a photo database and a social network dataset.

Original language	English (US)
Title of host publication	DMS 2010 - Proceedings of the 16th International Conference on Distributed Multimedia Systems
Pages	251-256
Number of pages	6
State	Published - Dec 1 2010
Event	16th International Conference on Distributed Multimedia Systems, DMS 2010 - Oak Brook, IL, United States Duration: Oct 14 2010 → Oct 16 2010

Other

Other	16th International Conference on Distributed Multimedia Systems, DMS 2010
Country	United States
City	Oak Brook, IL
Period	10/14/10 → 10/16/10

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Human-Computer Interaction
Software

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Jones, C., Armstrong, R., & Ma, K-L. (2010). Visualizing the commonalities between hierarchically structured data queries. In DMS 2010 - Proceedings of the 16th International Conference on Distributed Multimedia Systems (pp. 251-256)

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AB - In this paper we present an information visualization approach for visualizing the intersection between multiple feature hierarchies. In cases where a dataset has multiple complex features of interest, especially ones that have been hierarchically clustered, it is often very difficult to grasp the commonalities between them. Understanding the overlap between features can help researchers discover correlations or hot spots in their data. Our method steps back from actual data features, simplifying their representation, and presenting them in a fashion which facilitates interpretation of their inter-feature relationships. We call this new representation a knowledge graph, and we show how it can be used to investigate the overlap of multiple features in a photo database and a social network dataset.

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