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Personal and Ubiquitous Computing

, Volume 18, Issue 1, pp 223–238 | Cite as

A probabilistic approach to mining mobile phone data sequences

Original Article

Abstract

We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suffer from large amounts of noise and lack of ground truth. To address this complex data, we propose an unsupervised probabilistic topic model called the distant n-gram topic model (DNTM). The DNTM is based on latent Dirichlet allocation (LDA), which is extended to integrate sequential information. We define the generative process for the model, derive the inference procedure, and evaluate our model on both synthetic data and real mobile phone data. We consider two different mobile phone datasets containing natural human mobility patterns obtained by location sensing, the first considering GPS/wi-fi locations and the second considering cell tower connections. The DNTM discovers meaningful topics on the synthetic data as well as the two mobile phone datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model. The results show that the DNTM consistently outperforms LDA as the sequence length increases.

Keywords

Mobile Phone Topic Model Latent Dirichlet Allocation Unseen Data Mobile Phone Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was funded by the SNSF HAI project and the LS-CONTEXT project funded by Nokia Research. K. Farrahi also acknowledges the Socionical project and the Pervasive Computing Group at JKU, Linz. We thank Olivier Bornet (Idiap) for help with location data processing and visualization, Gian Paolo Perrucci (Nokia Research Lausanne) for insights on routine visualization, and Trinh-Minh-Tri Do (Idiap) for discussions on sequence modeling methods.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.JKU University LinzLinzAustria
  2. 2.Idiap Research InstituteMartignySwitzerland
  3. 3.EPFLLausanneSwitzerland

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