title: Value-cell bar charts for visualizing large transaction data sets.
abstract: One of the common problems businesses need to solve is how to use large volumes of sales histories, Web transactions, and other data to understand the behavior of their customers and increase their revenues. Bar charts are widely used for daily analysis, but only show highly aggregated data. Users often need to visualize detailed multidimensional information reflecting the health of their businesses. In this paper, we propose an innovative visualization solution based on the use of value cells within bar charts to represent business metrics. The value of a transaction can be discretized into one or multiple cells: high-value transactions are mapped to multiple value cells, whereas many small-value transactions are combined into one cell. With value-cell bar charts, users can 1) visualize transaction value distributions and correlations, 2) identify high-value transactions and outliers at a glance, and 3) instantly display values at the transaction record level. Value-Cell Bar Charts have been applied with success to different sales and IT service usage applications, demonstrating the benefits of the technique over traditional charting techniques. A comparison with two variants of the well-known Treemap technique and our earlier work on Pixel Bar Charts is also included.
authors.affiliation: Computer and Information Science Department, University of Konstanz, Konstanz, Germany. keim@informatik.uni-konstanz.de
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Keim
authors.forename: Ming C
authors.initials: MC
authors.lastname: Hao
authors.forename: Umeshwar
authors.initials: U
authors.lastname: Dayal
authors.forename: Martha
authors.initials: M
authors.lastname: Lyons
mesh_terms: D000066:Accounting / Q000379:methods*; D003116:Color; D003196:Computer Graphics; D003628:Database Management Systems; D016208:Databases, Factual; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*