Skip to main content
View expanded cover

International Workshop on Mining Ubiquitous and Social Environments

International Workshop on Modeling Social Media

MUSE 2013, MSM 2013: Mining, Modeling, and Recommending 'Things' in Social Media pp 116–130Cite as

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

  • 845 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-14723-9_7
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   39.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-14723-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   49.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    Device Analyzer website: http://deviceanalyzer.cl.cam.ac.uk/.

  2. 2.

    SL4A can be found at: http://code.google.com/p/android-scripting.

  3. 3.

    The stream container and processors that have been written to preprocess the data for both tasks are available online at: http://sfb876.tu-dortmund.de/mobidata.

References

  1. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10(4), 255–268 (2006)

    CrossRef  Google Scholar 

  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. 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 2012

    Google Scholar 

  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 2012

    Google Scholar 

  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. 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. 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. 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)

    CrossRef  Google Scholar 

  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 2012

    Google Scholar 

  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. 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. 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. Bockermann, C., Blom, H.: The streams framework. Technical report 5, TU Dortmund University, December 2012

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Piatkowski, N., Lee, S., Morik, K.: Spatio-temporal random fields: compressible representation and distributed estimation. Mach. Learn. 93(1), 115–139 (2013)

    CrossRef  MATH  MathSciNet  Google Scholar 

  17. Kschischang, F., Frey, B., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theory 47(2), 498–519 (2001)

    CrossRef  MATH  MathSciNet  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computer Science, LS8, TU Dortmund University, 44227, Dortmund, Germany

    Nico Piatkowski & Katharina Morik

  2. Department of Computer Science, LS12, TU Dortmund University, 44227, Dortmund, Germany

    Jochen Streicher & Olaf Spinczyk

Authors
  1. Nico Piatkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jochen Streicher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olaf Spinczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katharina Morik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nico Piatkowski .

Editor information

Editors and Affiliations

  1. University of Kassel, Kassel, Germany

    Martin Atzmueller

  2. Microsoft, Beijing, China

    Alvin Chin

  3. University of Kassel, Kassel, Germany

    Christoph Scholz

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Piatkowski, N., Streicher, J., Spinczyk, O., Morik, K. (2015). Open Smartphone Data for Structured Mobility and Utilization Analysis in Ubiquitous Systems. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14723-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14722-2

  • Online ISBN: 978-3-319-14723-9

  • eBook Packages: Computer ScienceComputer Science (R0)