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Issue No. 02 - Feb. (2016 vol. 28)
ISSN: 1041-4347
pp: 551-565
Huiqi Hu , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Guoliang Li , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Zhifeng Bao , Computer Science & Info Tech, RMIT University, Australia
Jianhua Feng , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Yongwei Wu , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Zhiguo Gong , Department of Computer and Information Science, University of Macau, China
Yaoqiang Xu , East China Grid
ABSTRACT
With the development of location-based services (LBS), LBS users are generating more and more spatio-textual data, e.g., checkins and attraction reviews. Since a spatio-textual entity may have different representations, possibly due to GPS deviations or typographical errors, it calls for effective methods to integrate the spatio-textual data from different data sources. In this paper, we study the problem of top-$_$k$_$ spatio-textual similarity join (Topk-STJoin), which identifies the $_$k$_$ most similar pairs from two spatio-textual data sets. One big challenge in Topk-STJoin is to efficiently identify the top- $_$k$_$ similar pairs by considering both textual relevancy and spatial proximity. Traditional join algorithms that consider only one dimension (textual or spatial) are inefficient because they cannot utilize the pruning ability on the other dimension. To address this challenge, we propose a signature-based top-$_$k$_$ join framework. We first generate a spatio-textual signature set for each object such that if two objects are in the top- $_$k$_$ similar pairs, their signature sets must overlap. With this property, we can prune large numbers of dissimilar pairs without common signatures. We find that the order of accessing the signatures has a significant effect on the performance. So, we compute an upper bound for each signature and propose a best-first accessing method that preferentially accesses signatures with large upper bounds while those pairs with small upper bounds can be pruned. We prove the optimality of our best-first accessing method. Next, we optimize the spatio-textual signatures and propose progressive signatures to further improve the pruning power. Experimental results on real-world datasets show that our algorithm achieves high performance and good scalability, and significantly outperforms baseline approaches.
INDEX TERMS
Upper bound, Complexity theory, Sorting, Global Positioning System, Electronic mail, Spatial indexes
CITATION

H. Hu et al., "Top-k Spatio-Textual Similarity Join," in IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 2, pp. 551-565, 2016.
doi:10.1109/TKDE.2015.2485213
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