Extracting Service Process Models from Location Data

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 307)

Abstract

Services are today over 70% of the Gross National Product in most developed countries. The productivity improvement of services is increasingly important and it relies heavily on a deep understanding of the service processes. However, how to collect data from services has been a problem and service data is largely missing in national statistics, which brings challenges to service process modelling.

This work aims to simplify the procedure of automated process modelling, and focuses on modelling generic service processes that are location-aware. An approach based on wireless indoor positioning is developed to acquire the minimum amount of location-based process data that can be used to automatically extract the process models.

The extracted models can be further used to analyse the possible improvements of the service processes. This approach has been tested and used in dental care clinics. Besides, the automated modelling approach can be used to greatly improve the traditional process modelling in various other service industries.

Keywords

Process modelling Service process Location-based Automated 

References

  1. 1.
    Azizyan, M., Constandache, I., Roy Choudhury, R.: SurroundSense: mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, pp. 261–272. ACM (2009)Google Scholar
  2. 2.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies Proceedings, INFOCOM 2000, vol. 2, pp. 775–784 (2000)Google Scholar
  3. 3.
    Baniukevic, A., Jensen, C.S., Lu, H.: Hybrid indoor positioning with Wi-Fi and Bluetooth: architecture and performance. In: 2013 IEEE 14th International Conference on Mobile Data Management, vol. 1, pp. 207–216 (2013)Google Scholar
  4. 4.
    Basu, S., Pascali, L., Schiantarelli, F., Serven, L.: Productivity and the welfare of nations. NBER Working Paper No. 17971, pp. 1–68 (2012)Google Scholar
  5. 5.
    Blum, T., Padoy, N., Feußner, H., Navab, N.: Workflow mining for visualization and analysis of surgeries. Int. J. Comput. Assist. Radiol. Surg. 3(5), 379–386 (2008)CrossRefGoogle Scholar
  6. 6.
    Bose, R.J.C., Mans, R.S., van der Aalst, W.M.: Wanna improve process mining results? In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 127–134. IEEE (2013)Google Scholar
  7. 7.
    Constandache, I., Choudhury, R.R., Rhee, I.: Towards mobile phone localization without war-driving. In: 2010 Proceedings of INFOCOM, pp. 1–9. IEEE (2010)Google Scholar
  8. 8.
    Dardari, D., Closas, P., Djurić, P.M.: Indoor tracking: theory, methods, and technologies. IEEE Trans. Veh. Technol. 64(4), 1263–1278 (2015)CrossRefGoogle Scholar
  9. 9.
    Davenport, T.H.: Process Innovation: Reengineering Work Through Information Technology. Harvard Business Press, Boston (2015). ISO 690Google Scholar
  10. 10.
    Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)CrossRefGoogle Scholar
  11. 11.
    Halonen, R., Martikainen, O., Juntunen, K., Naumov, V.: Seeking efficiency and productivity in health care. In: 20th Americas Conference on Information Systems. AMCIS-0251-2014.R1 (2014)Google Scholar
  12. 12.
    Lim, H., Kung, L.C., Hou, J.C., Luo, H.: Zero-configuration, robust indoor localization: theory and experimentation. In: INFOCOM (2006)Google Scholar
  13. 13.
    Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1067–1080 (2007)CrossRefGoogle Scholar
  14. 14.
    Liu, H., Gan, Y., Yang, J., Sidhom, S., Wang, Y., Chen, Y., Ye, F.: Push the limit of WiFi based localization for smartphones. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, pp. 305–316. ACM (2012)Google Scholar
  15. 15.
    Martikainen, O., Halonen, R.: Model for the benefit analysis of ICT. In: 17th Americas Conference on Information Systems, AMCIS 2011, pp. 4–7 (2011)Google Scholar
  16. 16.
    Martikainen, O.: A method and a computer program product for controlling the execution of at least one application on or for a mobile electronic device, and a computer. Patent, EP 2758874 (2011)Google Scholar
  17. 17.
    Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare – a case study in a Dutch hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-92219-3_32CrossRefGoogle Scholar
  18. 18.
    Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: data challenges when answering frequently posed questions. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) KR4HC/ProHealth 2012. LNCS (LNAI), vol. 7738, pp. 140–153. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36438-9_10CrossRefGoogle Scholar
  19. 19.
    Meng, S., Dou, W., Zhang, X., Chen, J.: KASR: a keyword-aware service recommendation method on mapreduce for big data applications. IEEE Trans. Parallel Distrib. Syst. 25(12), 3221–3231 (2014)CrossRefGoogle Scholar
  20. 20.
    Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. Wirel. Netw. 10(6), 701–710 (2004)CrossRefGoogle Scholar
  21. 21.
    Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014)CrossRefGoogle Scholar
  22. 22.
    Palumbo, F., Barsocchi, P., Chessa, S., Augusto, J.C.: A stigmergic approach to indoor localization using Bluetooth Low Energy beacons. In: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2015)Google Scholar
  23. 23.
    Partington, A., Wynn, M., Suriadi, S., Ouyang, C., Karnon, J.: Process mining for clinical processes: a comparative analysis of four Australian hospitals. ACM Trans. Manage. Inf. Syst. (TMIS) 5(4), 19 (2015)Google Scholar
  24. 24.
    Pham, C., Plötz, T., Olivier, P.: A dynamic time warping approach to real-time activity recognition for food preparation. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 21–30. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16917-5_3CrossRefGoogle Scholar
  25. 25.
    Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)CrossRefGoogle Scholar
  26. 26.
    Rovani, M., Maggi, F.M., de Leoni, M., van der Aalst, W.M.: Declarative process mining in healthcare. Expert Syst. Appl. 42(23), 9236–9251 (2015)CrossRefGoogle Scholar
  27. 27.
    Schimm, G.: Mining most specific workflow models from event-based data. In: van der Aalst, W.M.P., Weske, M. (eds.) BPM 2003. LNCS, vol. 2678, pp. 25–40. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-44895-0_3CrossRefGoogle Scholar
  28. 28.
    Sen, S., Lee, J., Kim, K.H., Congdon, P.: Avoiding multipath to revive inbuilding WiFi localization. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 249–262. ACM (2013)Google Scholar
  29. 29.
    Sen, S., Radunovic, B., Choudhury, R.R., Minka, T.: You are facing the Mona Lisa: spot localization using PHY layer information. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 183–196. ACM (2012)Google Scholar
  30. 30.
    Solow, R.M.: Technical change and the aggregate production function. Rev. Econ. Stat. 39(3), 312–320 (1957)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Vera-Baquero, A., Colomo-Palacios, R., Molloy, O.: Business process analytics using a big data approach. IT Prof. 15(6), 29–35 (2013)CrossRefGoogle Scholar
  32. 32.
    Wan, J., O’Grady, M.J., O’Hare, G.M.: Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Pers. Ubiquit. Comput. 19(2), 287–301 (2015)CrossRefGoogle Scholar
  33. 33.
    Youssef, M., Agrawala, A.: The Horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, pp. 205–218. ACM (2005)Google Scholar
  34. 34.
    Zhang, Y., Martikainen, O., Pulli, P., Naumov, V.: Real-time process data acquisition with Bluetooth. In: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, Barcelona, Spain, vol. 2629 (2011)Google Scholar
  35. 35.
    Zhang, L.L., Rodrigues, B.: A tree unification approach to constructing generic processes. IIE Trans. 41(10), 916–929 (2009)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Aalto UniversityEspooFinland

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