Skip to main content
SpringerLink
Log in

Discovering User’s Background Information from Mobile Phone Data

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

Abstract

Data collected from mobile phone have potential knowledge to provide background information of a mobile phone user, such as work location, home location, job occupation, income, consumption and even lifestyle etc., which are quite valuable to many location-aware applications. In the existing research, there is relatively few commercial software or application systems to fully meet the requirements of effectively mining these personal behavioral characteristics. In the paper, we propose approaches to analyzing personal activity characteristics and mining behavioral regularity from mobile phone location information, automatically generating some semantic labels by integrating mobile phone log data with map data and web data, and location prediction for personalized advertising services. We use actual mobile phone data to perform the functions for discovering background information and demonstrate effectiveness of our approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bayir, M.A., Demirbas, M., Eagle, N.: Discovering spatiotemporal mobility profiles of cellphone users. In Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, pp. 1–9. IEEE Press (2009)

    Google Scholar 

  2. Bayir, M.A., Demirbas, M., Cosar, A.: A Web-based Personalized Mobility Service for Smartphone Applications. The Computer Journal 54(5), 800–814 (2011)

    Article  Google Scholar 

  3. Birant, D., Kut, A.: ST-DBSCAN: An Algorithm for Clustering Spatial-Temporal Data. Journal Data & Knowledge Engineering 60(1), 208–221 (2007)

    Article  Google Scholar 

  4. Eagle, N., Pentland, A.S.: Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)

    Article  Google Scholar 

  5. Eagle, N., Pentland, A.S., Lazer, D.: Inferring Friendship Network Structure by Using Mobile Phone Data. Proceedings of the National Academy of Sciences (PNAS) 106(36), 15274–15278 (2009)

    Article  Google Scholar 

  6. González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding Individual Human Mobility Patterns. Nature 453(5), 779–782 (2008)

    Article  Google Scholar 

  7. Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., Varshavsky, A.: Identifying important places in people’s lives from cellular network data. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 133–151. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Licoppe, C., Diminescu, D., Smoreda, Z., Ziemlicki, C.: Using Mobile Phone Geolocalisation for ‘Socio-geographical’ Analysis of Co-ordination, Urban Mobilities, and Social Integration Patterns. Journal of Economic & Social Geography (TESG) 99(5), 584–601 (2008)

    Google Scholar 

  9. Phithakkitnukoon, S., Dantu, R.: Predicting calls– new service for an intelligent phone. In: Krishnaswamy, D., Pfeifer, T., Raz, D. (eds.) MMNS 2007. LNCS, vol. 4787, pp. 26–37. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Phithakkitnukoon, S., Dantu, R.: CPL: Enhancing mobile phone functionality by call predicted list. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2008. LNCS, vol. 5333, pp. 571–581. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Phithakkitnukoon, S., Dantu, R.: Mobile social closeness and similarity in calling patterns. In Proceedings of IEEE Conference on Consumer Communications & Networking Conference (CCNC 2010) Special Session on Social Networking (SocNets), pp. 1–5. IEEE Press (2010)

    Google Scholar 

  12. Phithakkitnukoon, S., Horanont, T., Di Lorenzo, G., Shibasaki, R., Ratti, C.: Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A. (eds.) HBU 2010. LNCS, vol. 6219, pp. 14–25. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International School of Software, Wuhan University, Wuhan, 430079, China

    Rong Xie & Yuchen Wang

  2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China

    Yang Yue

Authors
  1. Rong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuchen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong Xie .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Martin Wirsing

  3. Southwest University, Chongqing, China

    Zili Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Xie, R., Yue, Y., Wang, Y. (2015). Discovering User’s Background Information from Mobile Phone Data. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25159-2_62

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation