Skip to main content

Estimation of Customer Activity Patterns in Open Malls by Means of Combining Localization and Process Mining Techniques

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1285))

Abstract

In this work we present a method to estimate the activity patterns made by shoppers in open malls based on localization information and process mining techniques. We present our smart phone application for logging information from sensors and a process mining system to discover what kind of activity pattern is made by the shoppers based in the key information of the localization which combine data mining, parallelization and monitoring techniques. Our system has been tested in a challenging scenario such as open mall (mixed classical mall and isolated shops in the street) where location is obtained by means of Global Positioning System (GPS) and a WiFi localization system that is helped by a parallel computing method to speed up the whole process. Activity patterns made by shoppers are obtained by process mining techniques which are applied to the data. The process models obtained provide hints that may help mall’s managers to take specific actions considering the activity patterns that customers usually perform while in the mall.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. van der Aalst, W., et al: Process mining manifesto. In: Business Process Management Workshops, pp. 169–194. Springer, Heidelberg (2012)

    Google Scholar 

  2. van der Aalst, W.M.P., et al: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops - BPM 2011 International Workshops, Clermont-Ferrand, France, 29 August 2011, Revised Selected Papers, Part I. Lecture Notes in Business Information Processing, vol. 99, pp. 169–194. Springer (2011). https://doi.org/10.1007/978-3-642-28108-2_19

  3. Bouziane, H.L., Pérez, C., Priol, T.: Extending software component models with the master-worker paradigm. Parallel Comput. 36(2), 86–103 (2010). https://doi.org/10.1016/j.parco.2009.12.012. http://www.sciencedirect.com/science/article/pii/S0167819109001343

    Article  MATH  Google Scholar 

  4. Chapela-Campa, D., Mucientes, M., Lama, M.: Mining frequent patterns in process models. Inf. Sci. 472, 235–257 (2019). https://doi.org/10.1016/j.ins.2018.09.011

    Article  Google Scholar 

  5. Google’s Activity Recognition API. https://developers.google.com/location-context/activity-recognition

  6. Görg, S., Bergmann, R.: Social workflows-vision and potential study. Inf. Syst. 50, 1–19 (2015). https://doi.org/10.1016/j.is.2014.12.007. http://www.sciencedirect.com/science/article/pii/S030643791400194X

    Article  Google Scholar 

  7. Hernández, N., Ocaña, M., Alonso, J.M., Kim, E.: Continuous space estimation: increasing wifi-based indoor localization resolution without increasing the site-survey effort. Sensors 17(1), 147–170 (2017). https://doi.org/10.3390/s17010147

    Article  Google Scholar 

  8. Vázquez-Barreiros, B., Mucientes, M., Lama, M.: ProDiGen: mining complete, precise and minimal structure process models with a genetic algorithm. Inf. Sci. 294, 315–333 (2015). https://doi.org/10.1016/j.ins.2014.09.057

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work has been funded by the Cátedra de Ingeniería Avanzada Escriba-no of the UAH under Catedra2017-005, partially supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF/FEDER program) through grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, RED2018-102641-T, and RYC-2016-19802 (Programa Ramón y Cajal, José M. Alonso). Also by the Galician Ministry of Education, University and Professional Training and the ERDF/FEDER program (ED431F2018/02, ED431C2018/29, ED431G2019/04 grants).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manuel Ocaña .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ocaña, M. et al. (2021). Estimation of Customer Activity Patterns in Open Malls by Means of Combining Localization and Process Mining Techniques. In: Bergasa, L.M., Ocaña, M., Barea, R., López-Guillén, E., Revenga, P. (eds) Advances in Physical Agents II. WAF 2020. Advances in Intelligent Systems and Computing, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-62579-5_3

Download citation

Publish with us

Policies and ethics