Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Anastasios Arvanitis , Institute for the Management of Information Systems - ¿Athena¿ R.C., Greece
Dimitris Sacharidis , Institute for the Management of Information Systems - ¿Athena¿ R.C., Greece
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.
Anastasios Arvanitis, Dimitris Sacharidis, "Probabilistic contextual skylines", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 273-284, doi:10.1109/ICDE.2010.5447887