An Efficient Association Rule Mining-Based Spatial Keyword Index

Lianyin Jia, Haotian Tang, Mengjuan Li, Bingxin Zhao, Shoulin Wei and Haihe Zhou
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Lianyin Jia: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Haotian Tang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Mengjuan Li: Library, Yunnan Normal University, China
Bingxin Zhao: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Shoulin Wei: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Haihe Zhou: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 2, 1-19

Abstract: Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.

Date: 2023
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