Efficient Progressive Skyline Computation

By Kian-Lee Tan, Pin-Kwang Eng, Beng Chin Ooi. Very Large Data Bases Conference, 2001

822 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8826c500c659f33c76d7341105f0b7816769abda

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Citations in the corpus

Selecting Stars: The k Most Representative Skyline Operator (2007, background): “[33] propose the firstprogressivetechnique that can output skyline points without having to scan the whole dataset.”
An energy-efficient mobile recommender system (2010, background): “Indeed, skyline computing, which retrieves non-dominated data points, has been extensively studied in the database literature [9, 11, 13, 18].”
Maximal Vector Computation in Large Data Sets (2005, background): “In [10, 12], bitmaps are explored for skyline evaluation, appropriate when the number of values possible along a dimension is small.”
Probabilistic Skylines on Uncertain Data (2007, methodology): “The first progressive technique that can output skyline points without scanning the whole dataset was delveloped by Tan et al. [ 27 ].”
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries (2002, methodology): “We also studied the benchmark databases that were used in the experiments presented in [ TEO01 ].”
Efficient Computation of the Skyline Cube (2005, methodology): “[21] presents the first progressive algorithms, namely Bitmap and Index method, which can output the skyline without having to scan the whole dataset.”
Preference SQL - Design, Implementation, Experiences (2002, methodology): “Recently various methods have been proposed to implement the skyline operator , which can be employed to compute Pareto-optimal sets in special cases ([BKS01], [TEO01]).”
Skyline with presorting (2003, unknown): “In [9, 10], skyline algorithms that exploit indexes are introduced.”
The spatial skyline queries (2006, methodology): “These algorithms utilize techniques such as divide-and-conquer [1], nearest neighbor search [6], sorting [2], and index structures [1, 10, 8] to answer the general skyline queries.”
An optimal and progressive algorithm for skyline queries (2003, background): “Tan et al. [ TEO01 ] propose progressive algorithms that can output skyline points without having to scan the entire data input.”
Foundations of Preferences in Database Systems (2002, methodology): “Efficient evaluation algorithms have been given in [4] and [22].”
Progressive skyline computation in database systems (2005, methodology): “[2001], (3) in Chomicki et al. [2003], (4-5) in Tan et al.”
Efficient Distributed Skylining for Web Information Systems (2004, background): “Thus the first algorithms and subsequent improvements all work on a central (multidimensional) index structure like R*-trees [20], certain partitioning schemes [21] or k-nearest-neighbor searches [13].”
Finding k-dominant skylines in high dimensional space (2006, background): “On the other hand, property P1 implies that it is sufficient to compare p against the set of free skyline points (instead of all the points in D) to detect if a point p is k-dominant.”