Mining spatial-temporal geoMobile data via feature distributional similarity graph

Arvind Narayanan, Saurabh Verma, Zhi Li Zhang

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

2 Scopus citations

Abstract

Mobile devices and networks produce abundant data that exhibit geo-spatial and temporal properties mainly driven by human behavior and activities. We refer to such data as geoMobile data. Mining such data to extract meaningful patterns that are reflective of collective user activities and behavior can benefit mobile network resource management as well as the design and operations of mobile applications and services. However, diverse feature distributions inherent in such data make such a task challenging. In this paper we advocate an approach based on advanced machine learning algorithms to transform original data matrices into a feature distributional similarity graph and extract “latent” patterns from complex structures of geoMobile data. Our analysis is further aided with a visualization technique. Using mobile access data records from an operational cellular carrier, we demonstrate the potentials of our proposed approach under multiple settings, and make some very interesting observations from the obtained results.

Original languageEnglish (US)
Title of host publicationMobiData 2016 - Proceedings of the 1st Workshop on Mobile Data, co-located with MobiSys 2016
PublisherAssociation for Computing Machinery, Inc
Pages13-18
Number of pages6
ISBN (Print)9781450343275
DOIs
StatePublished - Jun 30 2016
Event1st Workshop on Mobile Data, MobiData 2016, co-located with MobiSys 2016 - Singapore, Singapore
Duration: Jun 30 2016 → …

Publication series

NameMobiData 2016 - Proceedings of the 1st Workshop on Mobile Data, co-located with MobiSys 2016

Other

Other1st Workshop on Mobile Data, MobiData 2016, co-located with MobiSys 2016
CountrySingapore
CitySingapore
Period6/30/16 → …

Bibliographical note

Funding Information:
This research was supported in part by NSF grants CNS-1117536, CRI-1305237, CNS-1411636 and DTRA grant HDTRA1-14-1-0040 and DoD ARO MURI Award W911NF-12-1-0385.

Keywords

  • Call detail records
  • Feature distributions
  • Latent pattern extraction
  • Similarity graph
  • Spatial-temporal

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