Proceedings of the 17th International Conference on Data Engineering
Preferences; Putting More Knowledge into Queries
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Telescope: zooming to interesting skylines
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Iterative modification and incremental evaluation of preference queries
FoIKS'06 Proceedings of the 4th international conference on Foundations of Information and Knowledge Systems
Approaching the efficient frontier: cooperative database retrieval using high-dimensional skylines
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
An efficient skyline framework for matchmaking applications
Journal of Network and Computer Applications
Information Sciences: an International Journal
SkyView: a user evaluation of the skyline operator
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Skyline queries, front and back
ACM SIGMOD Record
Scalable skyline computation using a balanced pivot selection technique
Information Systems
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When selecting alternatives from large amounts of data, trade-offs play a vital role in everyday decision making. In databases this is primarily reflected by the top-k retrieval paradigm. But recently it has been convincingly argued that it is almost impossible for users to provide meaningful scoring functions for top-k retrieval, subsequently leading to the adoption of the skyline paradigm. Here users just specify the relevant attributes in a query and all suboptimal alternatives are filtered following the Pareto semantics. Up to now the intuitive concept of compensation, however, cannot be used in skyline queries, which also contributes to the often unmanageably large result set sizes. In this paper we discuss an innovative and efficient method for computing skylines allowing the use of qualitative trade-offs. Such trade-offs compare examples from the database on a focused subset of attributes. Thus, users can provide information on how much they are willing to sacrifice to gain an improvement in some other attribute(s). Our contribution is the design of the first skyline algorithm allowing for qualitative compensation across attributes. Moreover, we also provide an novel trade-off representation structure to speed up retrieval. Indeed our experiments show efficient performance allowing for focused skyline sets in practical applications. Moreover, we show that the necessary amount of object comparisons can be sped up by an order of magnitude using our indexing techniques.