Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems 2013
DOI: 10.1145/2463664.2465225
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I/O-efficient planar range skyline and attrition priority queues

Abstract: We study the static and dynamic planar range skyline reporting problem in the external memory model with block size B, under a linear space budget. The problem asks for an O(n/B) space data structure that stores n points in the plane, and supports reporting the k maximal input points (a.k.a. skyline) among the points that lie within a givenone of its edges is grounded, two variants arise: topopen for β2 = ∞ and left-open for α1 = −∞ (symmetrically bottom-open and right-open) queries.We present optimal static d… Show more

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Cited by 7 publications
(4 citation statements)
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“…However, the modifications to support skyline queries are novel and non-trivial. The same problem (dynamic I/O-efficient range skyline reporting) but with worst case guarantees (logarithmic query I/Os, logarithmic amortized update I/Os and linear space) has been presented in [6].…”
Section: Definition 1 (Dominance)mentioning
confidence: 99%

Skyline Queries in O(1) time?

Sioutas,
Tsichlas,
Kosmatopoulos
et al. 2017
Preprint
Self Cite
“…However, the modifications to support skyline queries are novel and non-trivial. The same problem (dynamic I/O-efficient range skyline reporting) but with worst case guarantees (logarithmic query I/Os, logarithmic amortized update I/Os and linear space) has been presented in [6].…”
Section: Definition 1 (Dominance)mentioning
confidence: 99%

Skyline Queries in O(1) time?

Sioutas,
Tsichlas,
Kosmatopoulos
et al. 2017
Preprint
Self Cite
“…Recently, Kejlberg-Rasmussen et al in [12] have studied the problem in the external memory model and have…”
Section: Special Notementioning
confidence: 99%
“…Besides, we calculate the "constant" through probabilistic analysis, and our experiments show our calculated constant is reliable for different sizes of data. Database community has shown tremendous interest in solving problems formulated similar to convex hulls such as designing algorithms for finding Skylines [22,13,4].…”
Section: Figure 1: Classification Example With 3 Classesmentioning
confidence: 99%
“…Data mining topics such as classification [1,2,14,15,19,24,25,29], clustering [38,28,27,26,24,8,7,5,3], frequent pattern mining [37,36,11], frequent sub-structure mining [11,24], regression [21,20], data cleaning [18,8,1], ranking [6], data warehousing [5], recommender systems [23,16,10], bio-informatics [19], outlier detection [22,13,4], nearest neighbors [17,9] and social networks [31,32], have been widely discussed in data management and prediction.…”
Section: Introductionmentioning
confidence: 99%