Browse Prior Art Database

Method to Represent the Distribution of Data Values in a Collection Using an Iconic, Abstract Representation within Table or List Views Disclosure Number: IPCOM000033099D
Original Publication Date: 2004-Nov-25
Included in the Prior Art Database: 2004-Nov-25
Document File: 5 page(s) / 73K

Publishing Venue



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.

This text was extracted from a PDF file.
At least one non-text object (such as an image or picture) has been suppressed.
This is the abbreviated version, containing approximately 27% of the total text.

Page 1 of 5

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

In event monitoring applications (an example application domain for this technique) users typically select a group of events to study them in more detail. In this particular case the user expects to find some form of regularity within the selected data. Therefore it is particularly relevant to represent any kind of pattern within data fields. For example, if the group of selected events all contain one value in field A it is interesting to see whether field B and C also contains only a single value, randomly distributed unique values, or a sequence of ascending values without duplications etc. Additionally it is especially interesting to know whether B and C show a similar distribution, such as ascending unique values in both fields. This situation can be recognized at a glance using icons representing the fact that the values in a field form an ascending sequence.

Different application domain might consider different kinds of patterns interesting, however the following examples are probably considered relevant in most domains:

a) all entries show the same value in a field
b) there is a very small number of different values (2 or 3 values)
c) all entries are unique
d) the entries completely cover a specified range of values
e) the entries show a pattern within a range (such as: every second value or only even numbers occur)
f) the values show a specific distribution. For example almost all entries are in a small range, but one value is very different

Detecting such regularities within a collection of data values is state-of-the-art. This publication concerns itself only with representing these regularities using abstract representations.

Representation of patterns within field values.

As described, the key idea of this article is to show patterns within field values, as users are less interested in individual values than in the distribution of field values within a group of selected entries. Depending on the application domain, certain cases of patterns are particularly interesting. A number of example representations for the list of common patterns are described below.

a) all field values within a group are identical

In this case there is no need for an iconic abstraction. The system would simply display the unique value and possibly a count of occurrences.

b) The field contains only a small number of different values.

For a small number of different values, the values are displayed as exhaustive list. A preferred implementation also indicates the number of occurrences of each value. For example, if a field contains only the numbers 1 and 2, this could be shown as


Page 2 of 5

fieldname: 1, 2


fieldname: 1 (32), 2 (11)

The second example indicates the number of occurrences for each of the two values. The screen space available dictates the appropriate maximum number of values to show. Typically, th...