This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
Probabilistic contextual skylines
Long Beach, CA, USA
March 01-March 06
ISBN: 978-1-4244-5445-7
Dimitris Sacharidis, Institute for the Management of Information Systems - ¿Athena¿ R.C., Greece
Anastasios Arvanitis, Institute for the Management of Information Systems - ¿Athena¿ R.C., Greece
Timos Sellis, 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.
Citation:
Dimitris Sacharidis, Anastasios Arvanitis, Timos Sellis, "Probabilistic contextual skylines," icde, pp.273-284, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), 2010
Usage of this product signifies your acceptance of the Terms of Use.