Systematic Review of Indexing Spatial Skyline Queries for Decision Support

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0

Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.

2020 ◽  
Vol 192 ◽  
pp. 105299 ◽  
Author(s):  
Marta Fort ◽  
J. Antoni Sellarès ◽  
Nacho Valladares

Author(s):  
Marlene Goncalves ◽  
Fabiola Di Bartolo

Skyline queries may be used to filter interesting data from a broad range of data. A Skyline query selects those data that are the best according to multiple user-defined criteria. A special case of Skyline queries are the Spatial Skyline Queries (SSQ). SSQ allow users to express preferences on the closeness between a set of data points and a set of query points. We study the problem of answering SSQ in presence of changing data, i.e., data whose values regularly change over a period of time. In this chapter, it is proposed an algorithm to evaluate SSQ on changing data. The proposed algorithm is able to avoid recomputation of the whole Skyline with each update on the data. Also, the performance of the proposed algorithm against state-of-the-art algorithms was empirically studied. The experimental study shows that the proposed algorithm may become 3 times faster than state-of-the-art algorithms.


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