Uncertain and imprecise datasets are more and more characterizing actual database applications. These kind of data are likely to be captured by so-called probabilistic data models, which are attracting a great deal of interest from a large community of database researchers. Effectively and efficiently computing OLAP data cubes over probabilistic data is a relevant research challenge that naturally derives from the popularity of uncertain and imprecise datasets. This because OLAP is able of supporting a number of analysis perspectives over such datasets, whit an even-more-critical impact with respect to the case of traditional datasets (e.g., relational databases). This paper provides a spectrum of research contributions focused on OLAP over uncertain and imprecise data, ranging from theoretical models to a critical analysis of state-of-the-art proposals and a discussion on novel research perspectives.
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