TY - JOUR
T1 - An image construction method for visualizing managerial data
AU - Zhang, Ping
N1 - Funding Information:
I thank Dr. Andrew B. Whinston and Dr. Peng Si Ow for their contributions to the early stage of this research. I thank my colleagues Drs. Kevin Crowston, Robert Heckman, and Steve Sawyer for their helpful remarks during the preparation of this paper. I thank the anonymous reviewers for their comments and suggestions for finalizing this paper. Ping Zhang is Assistant Professor at School of Information Studies, Syracuse University. She has published papers in the areas of information visualization, user interface studies, computer simulation, and technology-assisted education. She has received a teaching award from UT Austin and a best paper award from the International Academy for Information Management. Dr. Zhang has a PhD in Information Systems from the University of Texas at Austin, and MSc and BSc in Computer Science from Peking University, Beijing, China. Dr. Zhang is a member of the ACM SIGCHI, IEEE Computer Society, INFORMS, AIS, and IAIM.
PY - 1998/10
Y1 - 1998/10
N2 - High volume data with complicated relationships can render human decision-making a frustrating task. Computer-generated visualization is an approach that can assist decision-makers in gaining insight into the data so that eventually superior solutions can be developed. Current research in visualization has addressed how to deal with high volume data that have some inherent structures (such as hierarchy, network, or geographical relationships). Many management domains, however, have data that lack obvious structures to provide a base for computer-generated visualization. This paper reports a specially designed technique for visualizing such management data. Data objects involved in the decision-making tasks are assigned with geometry (called visual abstract) in Euclidean space. Then a set of image construction rules are applied to connect multiple visual abstracts into images that can be displayed on a computer screen. We use two business domains, manufacturing production planning and resource constrained project scheduling, to illustrate this visualization technique.
AB - High volume data with complicated relationships can render human decision-making a frustrating task. Computer-generated visualization is an approach that can assist decision-makers in gaining insight into the data so that eventually superior solutions can be developed. Current research in visualization has addressed how to deal with high volume data that have some inherent structures (such as hierarchy, network, or geographical relationships). Many management domains, however, have data that lack obvious structures to provide a base for computer-generated visualization. This paper reports a specially designed technique for visualizing such management data. Data objects involved in the decision-making tasks are assigned with geometry (called visual abstract) in Euclidean space. Then a set of image construction rules are applied to connect multiple visual abstracts into images that can be displayed on a computer screen. We use two business domains, manufacturing production planning and resource constrained project scheduling, to illustrate this visualization technique.
KW - Decision making support
KW - Information visualization
KW - Manufacturing production planning
KW - Project scheduling with resource constraints and cash flow
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U2 - 10.1016/S0167-9236(98)00050-5
DO - 10.1016/S0167-9236(98)00050-5
M3 - Article
AN - SCOPUS:0032178736
SN - 0167-9236
VL - 23
SP - 371
EP - 387
JO - Decision Support Systems
JF - Decision Support Systems
IS - 4
ER -