Interactive visual analysis of hierarchical enterprise data

Yu Hsuan Chan, Kimberly Keeton, Kwan-Liu Ma

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

5 Citations (Scopus)

Abstract

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
Pages180-187
Number of pages8
DOIs
StatePublished - Dec 1 2010
Event12th IEEE Conference on Commerce and Enterprise Computing, CEC 2010 - Shanghai, China
Duration: Nov 10 2010Nov 12 2010

Other

Other12th IEEE Conference on Commerce and Enterprise Computing, CEC 2010
CountryChina
CityShanghai
Period11/10/1011/12/10

Fingerprint

Ontology
Research laboratories
Graph in graph theory
Industry
Bottom-up
Hierarchical Data
Vision
Efficacy
Person
Filtering
Unknown
Demonstrate
Hierarchy

Keywords

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

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Chan, Y. H., Keeton, K., & Ma, K-L. (2010). Interactive visual analysis of hierarchical enterprise data. In Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010 (pp. 180-187). [5708410] https://doi.org/10.1109/CEC.2010.37

Interactive visual analysis of hierarchical enterprise data. / Chan, Yu Hsuan; Keeton, Kimberly; Ma, Kwan-Liu.

Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010. 2010. p. 180-187 5708410.

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

Chan, YH, Keeton, K & Ma, K-L 2010, Interactive visual analysis of hierarchical enterprise data. in Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010., 5708410, pp. 180-187, 12th IEEE Conference on Commerce and Enterprise Computing, CEC 2010, Shanghai, China, 11/10/10. https://doi.org/10.1109/CEC.2010.37
Chan YH, Keeton K, Ma K-L. Interactive visual analysis of hierarchical enterprise data. In Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010. 2010. p. 180-187. 5708410 https://doi.org/10.1109/CEC.2010.37
Chan, Yu Hsuan ; Keeton, Kimberly ; Ma, Kwan-Liu. / Interactive visual analysis of hierarchical enterprise data. Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010. 2010. pp. 180-187
@inproceedings{27da12395a2345c58d522c6982193c3c,
title = "Interactive visual analysis of hierarchical enterprise data",
abstract = "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.",
keywords = "Business intelligence, Knowledge management, Social networks, Visual analytics",
author = "Chan, {Yu Hsuan} and Kimberly Keeton and Kwan-Liu Ma",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/CEC.2010.37",
language = "English (US)",
isbn = "9780769542287",
pages = "180--187",
booktitle = "Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010",

}

TY - GEN

T1 - Interactive visual analysis of hierarchical enterprise data

AU - Chan, Yu Hsuan

AU - Keeton, Kimberly

AU - Ma, Kwan-Liu

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

KW - Business intelligence

KW - Knowledge management

KW - Social networks

KW - Visual analytics

UR - http://www.scopus.com/inward/record.url?scp=79952383569&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952383569&partnerID=8YFLogxK

U2 - 10.1109/CEC.2010.37

DO - 10.1109/CEC.2010.37

M3 - Conference contribution

AN - SCOPUS:79952383569

SN - 9780769542287

SP - 180

EP - 187

BT - Proceedings - 12th IEEE International Conference on Commerce and Enterprise Computing, CEC 2010

ER -