Interactive Visual Analysis of Hierarchical Enterprise Data
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 bottomup 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.
Visual Analytics Social networks Knowledge Management Business Intelligence
Yu-Hsuan Chan Kimberly Keeton Kwan-Liu Ma
University of California, Davis Hewlett-Packard Labs
国际会议
上海
英文
180-187
2010-11-10（万方平台首次上网日期，不代表论文的发表时间）