Abstract
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 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-occurrence of the identifier of the user and that of the device used. The data is best modelled 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 the printing infrastructure in an office environment.
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© 2005 Springer-Verlag Berlin Heidelberg
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Andreoli, JM., Bouchard, G. (2005). Probabilistic Latent Clustering of Device Usage. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_1
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DOI: https://doi.org/10.1007/11552253_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
Online ISBN: 978-3-540-31926-9
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