time. Other examples of application areas in which event sequences occur are user interface design (event types are different user commands), criminology (types of crime), biostatistics (different symptoms), etc. In each of these applications, the data consists of one or several event sequences. Note that an event sequence is different from a time series in that a time series describes a variable with a continuous value over time, whereas an event sequence consists of discrete events happening in more or less random fashion.
One interesting aspect of event sequences is the degree of similarity between two event types. The definition of similarity may vary depending on the application. For example, if the application is web browsing behavior, two web pages may be considered similar if they convey the same type of information. If the example is computer user interface design, two commands may be similar if they accomplish the same function. Such similarity information is useful in itself, as it provides insight into the data. The similarity of web pages may be used to determine where links may be appropriate. The similarity of computer user commands may be used to select alternative commands to be suggested to the computer user. Moreover, similarities between event types can be used in various ways to make querying the data set more useful.
Because of the increasing difficulty and expense of determining similarity between event types in large sets of event sequences, it is desirable to provide a method of determining similarity between event types by merely scanning and interpreting the data in the database. Such a method would do away with the need to compare the content of event types to determine their similarity.
Determining a degree of relative similarity between event types in event sequences by examining the context of the event types facilitates similarity analysis of large sets of event sequences.
An event sequence may be defined as an ordered collection of events from a finite set of event types, with eac