Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (U T K). Given uncertain preferences, that is, an approximate description of the weight values, the U T K query reports all options that may belong to the top-k set. A second version of the problem additionally reports the exact top-k set for each of the possible weight settings. We develop a scalable processing framework for both U T K versions, and demonstrate its efficiency using standard benchmark datasets.
In REF , a geometry arrangement method is proposed for top-k queries with uncertain scoring functions.
Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings
PVLDB
PVLDB
Computer Science