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Analysis of trajectories in mobile networks based on data about the network proximity

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Abstract

This paper is devoted to location-based mobile services. The movement (trajectory) data extraction from logs related to network proximity is considered. Usually, this type of pattern extraction (search) relates to trajectory databases containing geoposition information. We consider a model of context-aware computing (a context-aware browser) based on network proximity. A mobile phone is considered as a proximity sensor. The geoposition information is replaced with the network proximity. Any existing or specially created network node can be regarded as a sensor of presence that provides access to dynamically determined network content. The disclosure of the content depends on the set of rules describing the conditions of network’s proximity. An algorithm is given for calculating the trajectories in mobile networks based on information about the network’s proximity.

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References

  1. Jeung, H., Yiu, M., Zhou, X., Jensen, C., and Shen, H., Discovery of convoys in trajectory databases, J. Proc. VLDB Endowment, 2008, vol. 1, pp. 1068–1080.

    Google Scholar 

  2. Ester, N., Kriegel, H.-P., Sander, J., and Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Conf. on Knowledge Discovery and Data of Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining, 1996, pp. 226–231.

    Google Scholar 

  3. Kalnis, P., Mamoulis, N., and Bakiras, S., On discovering moving clusters in spatio-temporal data, Proc. Symp. Spat. Temp. Databases, 2005, pp. 364–381.

    Chapter  Google Scholar 

  4. Aung, H. and Tan, K.-L., Discovery of evolving convoys, scientific and statistical database management, Lect. Notes Comp. Sci., 2010, vol. 6187/2010, pp. 196–213. DOI:10.1007/978-3-642-13818-8-16

    Article  Google Scholar 

  5. Namiot, D. and Schneps-Schneppe, M., About location-aware mobile messages, Proc. Int. Conf. Exhib. on Next Generation Mobile Applications, Services and Technologies (NGMAST), 2011, pp. 14–16. DOI:10.1109/NGMAST.2011.19.

    Google Scholar 

  6. Tang, L., Zheng, Y., Yuan, J. Han, J., Leung, A., Hung, C., and Peng, W., On discovery of traveling companions from streaming trajectories. http://research.microsoft.com/pubs/156047/On%20Discovery%20of%20Traveling%20Companions%20from%20Streaming%20Trajectories.pdf. Retrieved: Aug, 2012.

    Google Scholar 

  7. Sander, J., Ester, M., Kriegel, H. P., and Xu, X. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 1998, vol. 2, no. 2, pp. 169–194.

    Article  Google Scholar 

  8. Namiot, D. and Sneps-Sneppe, M., Proximity as a service, Proc. 2nd Baltic Congress on Future Internet Communications (BCFIC), 2012, pp. 199–205.

    Google Scholar 

  9. Daradkeh, Y., Namiot, D., and Sneps-Sneppe, M., Context-aware browsing for hyper-local news data, Int. J. Interactive Mobile Technol. (iJIM), 2012, vol. 6, no. 3, pp. 13–17. DOI: 10.3991/ijim.v6i2.2053.

    Google Scholar 

  10. Namiot, D., Context-aware browsing a practical approach, Proc. 6th Int. Conf. on Next Generation mobile Applications, services and technologies (NGMAST), 2012, pp. 18–23. DOI: 10.1109/NGMAST.2012.13.

    Google Scholar 

  11. Friedman-Hill, E., Jess in Action: Rule-Based Systems in Java, Greenwich, CT: Manning, 2003.

    Google Scholar 

  12. Pentland, A., Choudhury, T., Eagle, N., and Singh, P., Human dynamics: computation for organizations, Pattern Recogn. Lett., 2004, vol. 26, pp. 503–511.

    Article  Google Scholar 

  13. Funf Open Sensing Framework. http://funf.media.mit.edu

Download references

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Moscow State University, Leninskie Gory, Moscow, 119991, Russia

    D. Namiot

  2. Institute of Mathematics and Computer Science, University of Latvia, Raina blvd., Riga, LV-1459, Latvia

    M. Shneps-Shneppe

Authors
  1. D. Namiot
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  2. M. Shneps-Shneppe
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Correspondence to D. Namiot.

Additional information

Original Russian Text © D. Namiot, M. Shneps-Shneppe, 2013, published in Avtomatika i Vychislitel’naya Tekhnika, 2013, No. 3, pp. 48–60.

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Namiot, D., Shneps-Shneppe, M. Analysis of trajectories in mobile networks based on data about the network proximity. Aut. Control Comp. Sci. 47, 147–155 (2013). https://doi.org/10.3103/S014641161303005X

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  • DOI: https://doi.org/10.3103/S014641161303005X

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