Eliciting Matters – Controlling Skyline Sizes by Incremental Integration of User Preferences

Efficient skyline querying with variable user preferences on nominal attributes

“…There can be partial ordering on categorical attributes. [2,3,4,6,5,15,14,25] consider partially-ordered categorical attributes. In [2,3], each partially-ordered attribute is transformed into two-integer attributes so that conventional skyline algorithms can be applied.…”

“…In [15], a user or a customer can specify some values in nominal attributes as an equivalence class to denote the same "importance" for those values. [14] is an extension of [15]. In [14], whenever a user finds that there are a lot of irrelevant results for a query, s/he can modify the query by adding more conditions so that the result set is smaller to suit her/his need.…”

“…[14] is an extension of [15]. In [14], whenever a user finds that there are a lot of irrelevant results for a query, s/he can modify the query by adding more conditions so that the result set is smaller to suit her/his need. These works focus either on the effects of the query changes on the result size, or the reuse of skyline results when a query is refined in a progressive manner, and not on efficient query evaluation.…”

Optimal Preference Elicitation for Skyline Queries over Categorical Domains

“…First, we consider samples with the highest expected pruning cardinality, e.g., 'convertible' and 'sedan' for type, 'red' and 'blue' for color, and 'Ferrari' and 'Honda' for brand. Among these, we decide to obtain a preference elicitation on 'red' and 'blue' first, since its expected pruning cardinality (Theorem 1) is the highest, e.g., 1 2 (60 + 40). Once the elicitation result is obtained, for instance 'red' brand 'blue', for each object with 'red', we can prune out objects with 'blue' sharing the same remaining attribute values.…”

“…These preference models refer that qualitative models are more "intuitive" than quantitative models [11,14], which is consistent with our view. Meanwhile, Balke et al [3,4,1,2] studied how to use incomplete preference information for skyline queries: In particular, [3,4] studied how to identify skylines over user-specified partial orders. More recently, [1] extended the notion of equivalence to include the inter-attribute equivalence, and [2] discussed a sophisticated user interface in the cooperative process of identifying partial orders.…”