Dynamic skyline queries in metric spaces
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Skyline queries with constraints: Integrating skyline and traditional query operators
Data & Knowledge Engineering
Reverse skyline search in uncertain databases
ACM Transactions on Database Systems (TODS)
Feedback-driven result ranking and query refinement for exploring semi-structured data collections
Proceedings of the 13th International Conference on Extending Database Technology
Preferences in AI: An overview
Artificial Intelligence
On different types of fuzzy skylines
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
On possibilistic skyline queries
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
A survey of skyline processing in highly distributed environments
The VLDB Journal — The International Journal on Very Large Data Bases
Skyline queries in crowd-enabled databases
Proceedings of the 16th International Conference on Extending Database Technology
Finding skylines for incomplete data
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
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Recently, there has been much interest in processing skyline queries for various applications that include decision making, personalized services, and search pruning. Skyline queries aim to prune a search space of large numbers of multi-dimensional data items to a small set of interesting items by eliminating items that are dominated by others. Existing skyline algorithms assume that all dimensions are available for all data items. This paper goes beyond this restrictive assumption as we address the more practical case of involving incomplete data items (i.e., data items missing values in some of their dimensions). In contrast to the case of complete data where the dominance relation is transitive, incomplete data suffer from non-transitive dominance relation which may lead to a cyclic dominance behavior. We first propose two algorithms, namely, "Replacement" and "Bucket" that use traditional skyline algorithms for incomplete data. Then, we propose the "ISkyline" algorithm that is designed specifically for the case of incomplete data. The "ISkyline" algorithm employs two optimization techniques, namely, virtual points and shadow skylines to tolerate cyclic dominance relations. Experimental evidence shows that the "ISkyline" algorithm significantly outperforms variations of traditional skyline algorithms.