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Probabilistic latent clustering of device usage

Jean-Marc Andreoli, Guillaume Bouchard
We investigate an application of probabilistic latent semantics to the problem of device usage analysis in an infrastructure in which multiple users have access to a shared pool of devices capable of delivering different kinds of service and service levels. Each invocation of a service by a user, called a job, is assumed to be logged simply as a co-occurence of the identifier of the user and that of the device used. The data is best modeled by assuming that multiple latent variables (instead of a single one as in traditional PLSA) satisfying different types of constraints explain the observed variables of a job. We discuss the application of our model to a printing infrastructure in an office environment. Here, a job is assumed to be generated by two independent factors: the location of the user and the service class of the job (eg. black and white vs. colour, high volume vs. low volume etc.).
IDA 2005, the 6th International Symposium on Intelligent Data Analysis, Madrid, Spain, September 8-10, 2005.


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