TY - JOUR
T1 - Example-based Spatial Pattern Matching
AU - Chen, Yue
AU - Feng, Kaiyu
AU - Cong, Gao
AU - Kiah, Han Mao
N1 - Publisher Copyright:
© 2022, VLDB Endowment. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The prevalence of GPS-enabled mobile devices and location-based services yield massive volume of spatial objects where each object contains information including geographical location, name, address, category and other attributes. This paper introduces a novel type of query termed example-based spatial pattern matching (EPM) query. It takes as input a set of spatial objects, each of which is associated with one or more keywords and a location. These objects serve as an example that depicts the spatial pattern that users want to retrieve. The EPM query returns all sets of objects that match the spatial pattern. The EPM query can be used for applications like urban planning, scene recognition and similar region search. We propose an efficient algorithm and three pruning techniques to answer EPM queries. Furthermore, we provide an approximation guarantee for intermediate results of the algorithm. Our experimental evaluations on four real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithm and techniques.
AB - The prevalence of GPS-enabled mobile devices and location-based services yield massive volume of spatial objects where each object contains information including geographical location, name, address, category and other attributes. This paper introduces a novel type of query termed example-based spatial pattern matching (EPM) query. It takes as input a set of spatial objects, each of which is associated with one or more keywords and a location. These objects serve as an example that depicts the spatial pattern that users want to retrieve. The EPM query returns all sets of objects that match the spatial pattern. The EPM query can be used for applications like urban planning, scene recognition and similar region search. We propose an efficient algorithm and three pruning techniques to answer EPM queries. Furthermore, we provide an approximation guarantee for intermediate results of the algorithm. Our experimental evaluations on four real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithm and techniques.
UR - http://www.scopus.com/inward/record.url?scp=85138010574&partnerID=8YFLogxK
U2 - 10.14778/3551793.3551815
DO - 10.14778/3551793.3551815
M3 - Conference article
AN - SCOPUS:85138010574
SN - 2150-8097
VL - 15
SP - 2572
EP - 2584
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 11
T2 - 48th International Conference on Very Large Data Bases, VLDB 2022
Y2 - 5 September 2022 through 9 September 2022
ER -