Data charting

Details Data charting Data charting

1998-06-24

2001-07-03

Alam, Hosain T. (Department: 2771)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C706S010000, C706S062000, C345S215000, C345S440000, C345S024000, C704S200000

Reexamination Certificate

active

06256628

ABSTRACT:

BACKGROUND OF THE INVENTION
The present invention relates in general to a method of charting data. More particularly, the present invention is directed to a method of charting data and determining an optimum range for display of the data.
Conventional methods of data charting typically include axis based charting of data points, where a data point is displayed in relation to one or two axes with associated scale values. A scale is associated with a chart axis in order to provide the mapping of data values to coordinates on a computer output device such as a display or printer. The simplest form of scale representation is a linear progression at a regular interval ranging from a minimum to a maximum value. This form is suitable for the charting of data points that are fairly evenly distributed across a scale range.
A refinement of the linear scale is the logarithmic scale, where interval values are progressively compressed on the scale as the magnitude gets higher. This is suitable for charting data where the majority of data points are clustered toward the lower end of the scale range, but some data points are significantly greater in magnitude. In this case, a simple linear scale compresses the majority of data points such that visually distinguishing between the points becomes difficult.
Where data point clustering occurs away from the extremities of the scale range, or where multiple distinct clusters appear, the above techniques do not offer a solution to the problem of displaying and examining in detail the clustered values. A few conventional solutions exclude the extremities of the scale range from the displayed scale range and expand the portion of the scale range pertaining to clustered data values. However, this technique loses the context of the clustered data within the overall data set, and does not address the situation where more than one clustered set of data points exist.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to provide a method, employing multiple data range segments on an axis, to allow for selective detailed display of one or more clustered sets of data points, while retaining the context of each data cluster within a data set.
Accordingly, the present invention provides a method of displaying clustered data points in detail and in context within a data set including the following steps: identifying one or more clustered sets of data points within a data set, each data set including a plurality of data points, determining respective data range segments each cluster of data points, and displaying the data points in each segment such that each segment is mutually distinguishable.
Preferably, the determining step determines two or more data range segments. The scale interval value for one of said data range segments is modifiable to differ from one or more of said other data range segments.
Preferably, the identifying and determining steps include, in response to user interaction, determining one or more data range segments relating to data clusters following visual data cluster identification by the user.
The method also includes the step of: in response to user interaction, adjusting existing data range segments.
The invention is particularly useful in, for example, viewing charts showing CPU utilization over time. In such a case, important data points are often compressed into an area occupying as little as 15% of the utilization axis of the chart. Conventionally, the only option to examine the area in detail is to fill the display with the data. Although it is common for charting packages to allow such a display of only part of the data range, the ability to see the critical portion within context is also important.
This invention is also useful for Internet World Wide Web page providers who wish to display data, such as stock price movements over time, where the detail of price movement over a relatively long time swamps the detail of rapid price movements. The present invention allows such page providers to show detailed movements and allows user exploration of data without the need to subset data through, for example, query type user interaction.


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J.A. Hartigan, Statistical Theory in Clustering, Journal of Classification, 2:63-76 (1985).
Charles T. Zahn, Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters, IEEE Transactions on Computers, vol. C-20, No. 1, Jan. 1971, pp. 68-86.
J.C. Gower and G.J.S. Ross, Minimum Spanning Trees and Single Linkage Cluster Analysis, Rothamsted Experimental Station, pp. 54-63.

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