Visualizing Categorical Time Series Data with Applications to Computer and Communications Network Traces

Abstract

Visualization tools allow scientists to

comprehend very large data sets and to

discover relationships which are

otherwise difficult to detect.

Unfortunately, not all types of data can

be visualized easily using existing tools.

In particular, long sequences of

nonnumeric data cannot be visualized

adequately. Examples of this type of

data include trace files of computer

performance information, the

nucleotides in a genetic sequence, a

record of stocks traded over a period

of years, and the sequence of words in

this document. The term categorical

time series is defined and used to

describe this family of data. When

visualizations designed for numerical

time series are applied to categorical

time series, the distortions which result

from the arbitrary conversion of

unordered categorical values to totally

ordered numerical values can be

profound. Examples of this

phenomenon are presented and

explained. Several new, general

purpose techniques for visualizing

categorical time series data have been

developed as part of this work and

have been incorporated into the Chitra

performance analysis and visualization

system. All of these new visualizations

can be produced in O(n) time. The new

visualizations for categorical time series

provide general purpose techniques for

visualizing aspects of categorical data

which are commonly of interest. These

include periodicity, stationarity,

cross-correlation, autocorrelation, and

the detection of recurring patterns. The

effective use of these visualizations is

demonstrated in a number of

application domains, including

performance analysis, World Wide

Web traffic analysis, network routing

simulations, document comparison,

pattern detection, and the analysis of

the performance of genetic algorithms.