Discerning behavioral properties by analyzing transaction logs (extended abstract)
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
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Providers of web-based services are interested in monitoring the usage of their services in combination with those of other providers. The identification of services frequently accessed together may be valuable as a basis for strategic collaboration among their owners.We propose data mining to discover services of different providers which could complement one another, based on their usage. In particular, we model the activities of a user as a sequence of service invocations recorded in a log, on which pattern discovery techniques can be applied. However, we claim that conventional sequence mining is not adequate for this type of application. This is because, conventional mining concentrates on frequent (or infrequent) patterns of access, while we also require a notion of the consistency of these access patterns as a basis for collaboration.In this paper, we present a model for constructing patterns that depict consistently used sequences of activities. This model is general enough to be applied to any system of autonomous entities, where relationships between entities are dynamic. For testing our model, we have analyzed the behavior of users in a newsgroup, in order to determine consistent patterns in the way users respond to questions posed to the group.