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Method to Represent the Distribution of Data Values in a Collection Using an Iconic, Abstract Representation within Table or List Views

IP.com Disclosure Number: IPCOM000033099D
Original Publication Date: 2004-Nov-25
Included in the Prior Art Database: 2004-Nov-25

Publishing Venue

IBM

Abstract

In large lists of data objects (such as events) each field in the list entry might represent not one value, but a collection of values. For example in an event monitoring application a collection of 256 similar events might be summarized in a single summary row to conserve screen space. In such a case, the summarized events differ only in one or two fields, such as the field "destination port number". State of the art software would show such a summary row using a simple wildcard character such as an asterisk "*" for those fields containing more than one value in the collection. Wildcard representations occasionally indicate the number of unique values, but there is no additional indication of the actual structure of the data. Especially in event monitoring applications, though, it is very important to know whether the 256 field values form, e.g.,an ascending sequence, if only a few values occurred over and over again etc. The challenge is to represent the structure of the collection (in this example, the list of port numbers) without requiring significantly more screen real-estate than the wildcard character would have used such that this representation can be used within the event table representation. Not every possible pattern of port numbers is relevant and needs to be indicated. But certain patterns are -- again dependent on the application domain -- highly relevant. In current solutions such patterns are easily be overlooked as the actual data is hidden from the users. This publication teaches a method to use abstract representations for representing those special cases of patterns in the data that are relevant for the application domain. The abstract representations can be as simple as icons or can be small icon-like visualizations showing the actual distribution of the data values. The methods taught here are relevant not only for tables of data collections (useful for any kind of transaction processing system, such as in banking applications) Many applications require representation of a collection of values in a very small amount of screen space. An incomplete list of examples is given below: - detail views for a collection of values - any kind of visualization representing a collection of data might utilize the described techniques for labeling - a file system in an operating system can use the described abstractions to indicate patterns in a collection of file names - debugging tools (for software development) - typical event-like data representations occur also in web server logs etc.