Probabilistic contextual skylines

Abstract

Abstract-The skyline query returns the most interesting tuples according to a set of explicitly defined preferences among attribute values. This work relaxes this requirement, and allows users to pose meaningful skyline queries without stating their choices. To compensate for missing knowledge, we first determine a set of uncertain preferences based on user profiles, i.e., information collected for previous contexts. Then, we define a probabilistic contextual skyline query (p-CSQ) that returns the tuples which are interesting with high probability. We emphasize that, unlike past work, uncertainty lies within the query and not the data, i.e., it is in the relationships among tuples rather than in their attribute values. Furthermore, due to the nature of this uncertainty, popular skyline methods, which rely on a particular tuple visit order, do not apply for p-CSQs. Therefore, we present novel non-indexed and index-based algorithms for answering p-CSQs. Our experimental evaluation concludes that the proposed techniques are significantly more efficient compared to a standard block nested loops approach.

Efficient computation for probabilistic skyline over uncertain preferences

“…The independent object dominance assumption adopted in [16] to compute skyline probability on uncertain preferences was shown not valid in [17]. This leaves [17] as the only other work that deals with skyline probability computation over uncertain preferences.…”

“…Two works that deal with skyline computation on uncertain preferences are [16,17]. The independent object dominance assumption adopted in [16] to compute skyline probability on uncertain preferences was shown not valid in [17].…”

Possibilistic contextual skylines with incomplete preferences

“…We use an example taken from [16]. It consists of a relation with 3 attributes Price, Distance and Amenity about a set of hotels (Table I).…”

“…In the following, possibility theory [15] is used to model the uncertainty associated with preferences. The proposed approach may be paralleled with the probabilistic method studied in [16]. We use here possibility theory due to its ability to deal with incomplete information, and its qualitative nature that makes it suitable for the problem addressed.…”

A Fuzzy-Rule-Based Model for Handling Contextual Preference Queries

“…Very recently, uncertain contextual preferences have been addressed in 29,30 in the skyline queries setting * * . Namely, how to deal with the problem of missing information in users preferences (i.e., preferences are not specified for some specific context).…”