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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 747–766 | Cite as

A common subgraph correspondence mining framework for map search services

  • Wu LiuEmail author
  • Lingheng Zhu
  • Lingyang Chu
  • Huadong Ma
Article

Abstract

With the development of GPS, Internet, and mobile devices, the map searching services become an essential application in people’s lives. However, existing map searching services only support simple keywords based location search, which neglect users’ complex search requirements, such as searching a hotel surrounded by station, shopping mall, cinema, etc. In this paper, we propose a map searching framework which maps users’ complex search requirements into a graph pattern match problem. In this framework, the user’s query requirements are mapped into a undirected graph, where the vertexes indicate the searched locations, and the edges represent the distance between connected locations. In this way, we propose a common pattern searching algorithm to give the top k matches groups and arrange them using a similarity. The similarity is defined by multi-model information, i.e., the graph structure, location categories, review stars, and review counts. Moreover, this method also allows user to input ambiguous and uncertain query condition with sketching query map. To adapt the method to large-scale data, we also filter the candidate groups by effective pruning methods. The evaluations on Yelp dataset demonstrate the proposed method is effective and flexibility in both certain and uncertain query graphs.

Keywords

Map searching Graph pattern match Common pattern mining Multi-model information 

Notes

Acknowledgements

This work is partially supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 61720106007), the National Natural Science Foundation of China (No. 61602049), the NSFC-Guangdong Joint Fund (U1501254), and the Cosponsored Project of Beijing Committee of Education.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of Computing ScienceSimon Fraser UniversityVancouverCanada

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