Abstract
In this paper, we discuss the problem of organizing the different ways of computing the interestingness of a particular cell derived from a cube in the context of a hierarchical, multidimensional space. We start from an in-depth study of the interestingness aspects in the study of human behavior and include in our survey the approaches taken by computer-science efforts in the area of data mining and user recommendations. We move on to structure interestingness along different fundamental, high level aspects, and, due to their high-level nature, we also move towards much more concrete data-oriented definitions of interestingness aspects.
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Notes
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We do not distinguish between the terms session and exploration in what follows.
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In this implementation, the user belief is agnostic of measure values, and the metric therefore characterizes how surprising it is that the user visits this particular cell.
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Marcel, P., Peralta, V., Vassiliadis, P. (2019). A Framework for Learning Cell Interestingness from Cube Explorations. In: Welzer, T., Eder, J., Podgorelec, V., Kamišalić Latifić, A. (eds) Advances in Databases and Information Systems. ADBIS 2019. Lecture Notes in Computer Science(), vol 11695. Springer, Cham. https://doi.org/10.1007/978-3-030-28730-6_26
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