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Open Smartphone Data for Structured Mobility and Utilization Analysis in Ubiquitous Systems

  • Nico PiatkowskiEmail author
  • Jochen Streicher
  • Olaf Spinczyk
  • Katharina Morik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8940)

Abstract

The development and evaluation of new data mining methods for ubiquitous environments and systems requires real data that were collected from real users. In this work, we present an open smartphone utilization and mobility data set that was generated with several devices and participants during a 4-month study. A particularity of this data set is the inclusion of low-level operating system data. Additionally to the description of the data, we also describe the process of collection and the privacy measures we applied. To demonstrate the utility of the data, we evaluate the quality of generative spatio-temporal models for “apps” and network cells, since these are required as a building block in general predictions of the resource consumption of ubiquitous systems.

Keywords

Mobile Network Semantic Place Probabilistic Graphical Model Ubiquitous System Loopy Belief Propagation 
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 work has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Data Analysis”, project A1. We would also like to thank our collaboration partners from the EcoSense project at Aarhus University for providing technical support. Last but not least, we would like to thank all our participants for contributing to the data set.

References

  1. 1.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  2. 2.
    Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings of the 7th International Conference on Pervasive Services, ACM (2010)Google Scholar
  3. 3.
    Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T.-M.-T., Dousse, O., Eberle, J., Miettinen, M.: The mobile data challenge: big data for mobile computing research. In: Mobile Data Challenge by Nokia Workshop, in Conjunction with International Conference on Pervasive Computing, June 2012Google Scholar
  4. 4.
    Michaelis, S., Piatkowski, N., Morik, K.: Predicting next network cell IDs for moving users with discriminative and generative models. In: Mobile Data Challenge by Nokia Workshop in Conjunction with International Conference on Pervasive Computing, June 2012Google Scholar
  5. 5.
    Chon, Y., Talipov, E., Shin, H., Cha, H.: Mobility prediction-based smartphone energy optimization for everyday location monitoring. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 2011, pp. 82–95. ACM, New York (2011)Google Scholar
  6. 6.
    Nath, S.: ACE: exploiting correlation for energy-efficient and continuous context sensing. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 29–42. ACM, New York (2012)Google Scholar
  7. 7.
    Schulman, A., Navda, V., Ramjee, R., Spring, N., Deshpande, P., Grunewald, C., Jain, K., Padmanabhan, V.N.: Bartendr: A practical approach to energy-aware cellular data scheduling. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, MobiCom 2010, pp. 85–96. ACM, New York (2010)Google Scholar
  8. 8.
    Fricke, P., Jungermann, F., Morik, K., Piatkowski, N., Spinczyk, O., Stolpe, M., Streicher, J.: Towards adjusting mobile devices to user’s behaviour. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds.) MUSE/MSM 2010. LNCS, vol. 6904, pp. 99–118. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Huang, C.M., Ying, J.J.-C., Tseng, V.: Mining users’ behavior and environment for semantic place prediction. In: Mobile Data Challenge by Nokia Workshop in Conjunction with International Conference on Pervasive Computing, June 2012Google Scholar
  10. 10.
    Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2011, pp. 54–63. ACM, New York (2011)Google Scholar
  11. 11.
    Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R.P., Mao, Z.M., Yang, L.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the 8th IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, CODES/ISSS 2010, pp. 105–114. ACM, New York (2010)Google Scholar
  12. 12.
    Dong, M., Zhong, L.: Self-constructive high-rate system energy modeling for battery-powered mobile systems. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys 2011, pp. 335–348. ACM, New York (2011)Google Scholar
  13. 13.
    Bockermann, C., Blom, H.: The streams framework. Technical report 5, TU Dortmund University, December 2012Google Scholar
  14. 14.
    Kjærgaard, M.B., Blunck, H.: Unsupervised power profiling for mobile devices. In: Puiatti, A., Gu, T. (eds.) MobiQuitous 2011. LNICST, vol. 104, pp. 138–149. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Wille, A., Bühlmann, P.: Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genet. Mol. Bio. 5, 1–32 (2006)Google Scholar
  16. 16.
    Piatkowski, N., Lee, S., Morik, K.: Spatio-temporal random fields: compressible representation and distributed estimation. Mach. Learn. 93(1), 115–139 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Kschischang, F., Frey, B., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theory 47(2), 498–519 (2001)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nico Piatkowski
    • 1
    Email author
  • Jochen Streicher
    • 2
  • Olaf Spinczyk
    • 2
  • Katharina Morik
    • 1
  1. 1.Department of Computer Science, LS8TU Dortmund UniversityDortmundGermany
  2. 2.Department of Computer Science, LS12TU Dortmund UniversityDortmundGermany

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