Shooting Stars in the Sky: An Online Algorithm for Skyline Queries

By Donald Kossmann, Frank Ramsak, Steffen Thomas Rost. Very Large Data Bases Conference, 2002

1,000 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/10feecb5eebbb958439aabb2e10bd56739e315c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Skyline with presorting (2003, neutral): “In [9, 10], skyline algorithms that exploit indexes are introduced.”
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].”
Stabbing the sky: efficient skyline computation over sliding windows (2005, methodology): “The main memory algorithms may be found in [3, 17, 14], while the techniques related to databas e applications may be found in [4, 6, 15, 23, 27].”
Maximal Vector Computation in Large Data Sets (2005, methodology): “The shooting-stars algorithm [13] exploits R-trees and modifies nearest-neighbors approaches for finding skyline points progressively.”
Finding k-dominant skylines in high dimensional space (2006, methodology): “[9] proposed a Nearest Neighbor (NN) method to process skyline queries progressively.”
Efficient Computation of the Skyline Cube (2005, methodology): “A skyline computation algorithm based on the indexed dataset is first developed in [14].”
Progressive skyline computation in database systems (2005, methodology): “[ KRR02 ] propose the following elimination methods:”
Probabilistic Skylines on Uncertain Data (2007, methodology): “Kossmann et al. [16] presented another progressive algorithm based on the nearest neighbor search technique, which adopts a divide-and-conquer paradigm on the dataset.”
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.”
Selecting Stars: The k Most Representative Skyline Operator (2007, methodology): “full skyline) have been reported in the literature [4, 9, 13, 22, 29, 33].”
An optimal and progressive algorithm for skyline queries (2003, methodology): “Kossmann et al. [ KRR02 ] propose the following elimination methods: Laisser-faire: A main memory hash table stores the skyline points found so far.”