In this demonstration we present MYOLAP, a Java-based tool that allows OLAP analyses to be personalized and enhanced by expressing “soft” query constraints in the form of user preferences. MYOLAP is based on a novel preference algebra and a preference evaluation algorithm specifically devised for the OLAP domain. Preferences are formulated either visually or through an extension of the MDX language, and user interaction with the results is mediated by a visual graph-like structure that shows better-than relationships between different sets of data. The demonstration will show how analysis sessions can benefit from coupling ad-hoc preference constructors with the classical OLAP operators, and in particular how MYOLAP supports users in expressing preference queries, analyzing their results, and navigating datacubes.

Multidimensional databases are the core of business intelligence systems. Their users express complex OLAP queries, often returning large volumes of facts, sometimes providing little or no information. Thus, expressing preferences could be highly valuable in this domain. The OLAP domain is representative of an unexplored class of preference queries, characterized by three peculiarities: preferences can be expressed on both numerical and categorical domains; they can also be expressed on the aggregation level of facts; the space on which preferences are expressed includes both elemental and aggregated facts. In this paper, we present myOLAP, an approach for expressing and evaluating OLAP preferences, devised by taking into account the three peculiarities above. We first propose a preference algebra where users are enabled to express their preferences, besides on attributes and measures, also on the aggregation level of facts, for instance, by stating that monthly data are preferred to yearly and daily data. Then, with respect to preference evaluation, we propose an algorithm called WeSt that relies on a novel graph representation where two types of domination between sets of facts may be expressed, which considerably improves efficiency. The approach is extensively tested for efficiency and effectiveness on real data, and compared against two other approaches in the literature.