SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
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
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Towards multidimensional subspace skyline analysis
ACM Transactions on Database Systems (TODS)
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Multi-objective query processing for database systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximate Evaluation of Range Nearest Neighbor Queries with Quality Guarantee
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Reaching the Top of the Skyline: An Efficient Indexed Algorithm for Top-k Skyline Queries
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
The VLDB Journal — The International Journal on Very Large Data Bases
Z-SKY: an efficient skyline query processing framework based on Z-order
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Data & Knowledge Engineering
Efficiently evaluating skyline queries on RDF databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
SkyMap: a trie-based index structure for high-performance skyline query processing
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
CAREY: ClimAtological contRol of EmergencY regions
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Malleability-Aware skyline computation on linked open data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Top-k linked data query processing
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Criteria that induce a Skyline naturally represent user's preference conditions useful to discard irrelevant data in large datasets. However, in the presence of high-dimensional Skyline spaces, the size of the Skyline can still be very large, making unfeasible for users to process this set of points. To identify the best points among the Skyline, the Top-k Skyline approach has been proposed. Top-k Skyline uses discriminatory criteria to induce a total order of the points that comprise the Skyline, and recognizes the best or top-k points based on these criteria. In this article the authors model queries as multi-dimensional points that represent bounds of VPT Vertically Partitioned Table property values, and datasets as sets of multi-dimensional points; the problem is to locate the k best tuples in the dataset whose distance to the query is minimized. A tuple is among the k best tuples whenever there is not another tuple that is better in all dimensions, and that is closer to the query point, i.e., the k best tuples correspond to the k nearest points to the query that are incomparable or belong to the skyline. The authors name these tuples the k nearest neighbors in the skyline. The authors propose a hybrid approach that combines Skyline and Top-k solutions and develop two algorithms: TKSI and k-NNSkyline. The proposed algorithms identify among the skyline tuples, the k ones with the lowest values of the distance metric, i.e., the k nearest neighbors to the multi-dimensional query that are incomparable. Empirically, we study the performance and quality of TKSI and k-NNSkyline. The authors' experimental results show the TKSI is able to speed up the computation of the Top-k Skyline in at least 50% percent with respect to the state-of-the-art solutions, whenever k is smaller than the size of the Skyline. Additionally, the authors' results suggest that k-NNSkyline outperforms existing solutions by up to three orders of magnitude.