Density based Clustering over Location Based Services

Md Farhadur Rahman, Weimo Liu, Saad Bin Suhaim, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Location Based Services (LBS) have become extremely popular over the past decade, being used on a daily basis by millions of users. Instances of real-world LBS range from mapping services (e.g., Google Maps) to lifestyle recommendations (e.g., Yelp) to real-estate search (e.g., Redfin). In general, an LBS provides a public (often web-based) search interface over its backend database (of tuples with 2D geolocations), taking as input a 2D query point and returning k tuples in the database that are closest to the query point, where k is usually a small constant such as 20 or 50. Such a public interface is often called a k-Nearest-Neighbor, i.e., kNN, interface. In this paper, we consider a novel problem of enabling density based clustering over the backend database of an LBS using nothing but limited access to the kNN interface provided by the LBS. Specifically, a key limit enforced by most real-world LBS is a maximum number of kNN queries allowed from a user over a given time period. Since such a limit is often orders of magnitude smaller than the number of tuples in the LBS database, our goal here is to mine from the LBS a cluster assignment function f(·), such that for any tuple t in the database (which may or may not have been accessed), f(·) can produce the cluster assignment of t with high accuracy. We conduct a comprehensive set of experiments over benchmark datasets and popular real-world LBS such as Yahoo Flickr, Zillow, Redfin and Google Maps and demonstrate the effectiveness of our proposed techniques.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages461-469
Number of pages9
ISBN (Electronic)9781509065431
DOIs
Publication statusPublished - 16 May 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Other

Other33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period19/4/1722/4/17

Fingerprint

Location based services

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Rahman, M. F., Liu, W., Suhaim, S. B., Thirumuruganathan, S., Zhang, N., & Das, G. (2017). Density based Clustering over Location Based Services. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 461-469). [7929999] IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.103

Density based Clustering over Location Based Services. / Rahman, Md Farhadur; Liu, Weimo; Suhaim, Saad Bin; Thirumuruganathan, Saravanan; Zhang, Nan; Das, Gautam.

Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. p. 461-469 7929999.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rahman, MF, Liu, W, Suhaim, SB, Thirumuruganathan, S, Zhang, N & Das, G 2017, Density based Clustering over Location Based Services. in Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017., 7929999, IEEE Computer Society, pp. 461-469, 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, United States, 19/4/17. https://doi.org/10.1109/ICDE.2017.103
Rahman MF, Liu W, Suhaim SB, Thirumuruganathan S, Zhang N, Das G. Density based Clustering over Location Based Services. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society. 2017. p. 461-469. 7929999 https://doi.org/10.1109/ICDE.2017.103
Rahman, Md Farhadur ; Liu, Weimo ; Suhaim, Saad Bin ; Thirumuruganathan, Saravanan ; Zhang, Nan ; Das, Gautam. / Density based Clustering over Location Based Services. Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. pp. 461-469
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