Interactive visual analysis of hierarchical enterprise data

Yu Hsuan Chan, Kimberly Keeton, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


In this paper, we present an interactive visual technique for analyzing and understanding hierarchical data, which we have applied to analyzing a corpus of technical reports produced by a corporate research laboratory. The analysis begins by selecting a known entity, such as a topic, a report, or a person, and then incrementally adds other entities to the graph based on known relations. As this bottom-up knowledge building process proceeds, meaningful graph structure may appear and reveal previously unknown relations. The ontology of the data, which represents the types of entities in the data and all possible relations among them, is displayed as a guide to the analyst in the process. The analyst may interact with the ontology graph or the data graph directly. In addition, we provide a set of filtering, searching, and abstraction methods for the analyst to manage the complexity of the graph. In contrast to a top-down approach, which usually starts with an overview of the whole data set for exploration, a bottom-up approach is generally more efficient, because it often only touches a very small fraction of the data. We present several case studies to demonstrate the efficacy of this interactive graph-based analysis technique for both intra- and inter-hierarchy analysis.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010
Number of pages8
StatePublished - Dec 1 2010
Event12th IEEE Conference on Commerce and Enterprise Computing, CEC 2010 - Shanghai, China
Duration: Nov 10 2010Nov 12 2010


Other12th IEEE Conference on Commerce and Enterprise Computing, CEC 2010


  • Business intelligence
  • Knowledge management
  • Social networks
  • Visual analytics

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science


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