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Location- and keyword-based querying of geo-textual data: a survey

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Abstract

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.

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Notes

  1. https://twitter.com/

  2. https://www.flickr.com/

  3. https://www.instagram.com/

  4. https://foursquare.com/

  5. https://www.yelp.com/

  6. https://www.tripadvisor.com/

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Acknowledgements

This research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC. The authors would like to thank the anonymous reviewers for providing constructive feedback and valuable suggestions.

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  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Zhida Chen & Gao Cong

  2. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Lisi Chen

  3. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Christian S. Jensen

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  1. Zhida Chen
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  2. Lisi Chen
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  3. Gao Cong
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Correspondence to Gao Cong.

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Chen, Z., Chen, L., Cong, G. et al. Location- and keyword-based querying of geo-textual data: a survey. The VLDB Journal 30, 603–640 (2021). https://doi.org/10.1007/s00778-021-00661-w

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