Authors:
Ruei Sian Jheng
1
;
En Tzu Wang
2
and
Arbee L. P. Chen
3
Affiliations:
1
National Tsing-Hua University, Taiwan
;
2
Industrial Technology Research Institute, Taiwan
;
3
National Chengchi University, Taiwan
Keyword(s):
Top-K Queries, Range Queries, Skyline Queries, Reverse Skyline Queries, Quad-tree Index.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Data Management and Quality
;
Data Structures and Data Management Algorithms
Abstract:
Given a set of criteria, an object o is defined to dominate another object o' if o is no worse than o' in each
criterion and has better outcomes in at least a specific criterion. A skyline query returns each object that is
not dominated by any other objects. Consider a scenario as follows. Given three types of datasets, including
residents in a city, existing restaurants in the city, and candidate places for opening new restaurants in the
city, where each restaurant and candidate place has its respective rank on a set of criteria, e.g., convenience
of parking, we want to find the top-k candidate places that have the most potential customers. The potential
customers of a candidate place is defined as the number of residents whose distance to this candidate is no
larger than a given distance r and also regard this candidate as their skyline restaurants. In this paper, we
propose an efficient method based on the quad-tree index and use four pruning strategies to solve this
problem. A se
ries of experiments are performed to compare the proposed method with a straightforward
method using the R-tree index. The experiment results demonstrate that the proposed method is very
efficient, and the pruning strategies very powerful.
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