DBLOC: Density based clustering over location based services

Yeshwanth D. Gunasekaran, Md Farhadur Rahman, Sona Hasani, Nan Zhang, Gautam Das

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

1 Scopus citations


Location Based Services (LBS) have become extremely popular over the past decade. Popular LBS run the entire gamut from mapping services (such as Google Maps) to restaurants reviews (such as Yelp) and real-estate search (such as Zillow). The backend database of these applications can be a rich data source for geospatial and commercial information such as Point-Of-Interest (POI) locations, reviews, ratings, user geo-distributions, etc. However, access to the backend database is often restricted by a public query interface (often web-based) provided by the LBS owners. In most cases the public search interface of these applications can be abstractly modeled as kNN interface, taking a geolocation (i.e., latitude and longitude) as input and returning top-k POI's that are closest to the query point, where k is a small constant such as 50 or 100. Because of this restriction it becomes extremely difficult for third-party users to perform analytics or mining over LBS. We demonstrate DBLOC, a web-based system that enables analytics over the LBS by using nothing but limited access to kNN interface provided by the LBS. Specifically, using DBLOC the users can perform density based clustering over the backend database of LBS. Due to query rate limit constraint-i.e., maximum number of kNN queries a user/IP address can issue over a specific period of time, it is often impossible to access all the tuples in backend database of an LBS. Thus, DBLOC aims 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 also demonstrate how DBLOC enables the users to further analyze the discovered clusters in order to mine interesting intra/inter cluster information.

Original languageEnglish (US)
Title of host publicationSIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data
EditorsGautam Das, Christopher Jermaine, Ahmed Eldawy, Philip Bernstein
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450317436
StatePublished - May 27 2018
Event44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 - Houston, United States
Duration: Jun 10 2018Jun 15 2018

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Other44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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