A Subjective Interestingness measure for Business Intelligence explorations
Alexandre Chanson,
Ben Crulis,
Nicolas Labroche
et al.
Abstract:This paper addresses the problem of defining a subjective interestingness measure for BI exploration. Such a measure involves prior modeling of the belief of the user. The complexity of this problem lies in the impossibility to ask the user about the degree of belief in each element composing their knowledge prior to the writing of a query. To this aim, we propose to automatically infer this user belief based on the user's past interactions over a data cube, the cube schema and other users' past activities. We… Show more
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