Skip to main content

E-Government Services: Comparing Real and Expected User Behavior

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2017)

Abstract

E-government web services are becoming increasingly popular among citizens of various countries. Usually, to receive a service, the user has to perform a sequence of steps. This sequence of steps forms a service rendering process. Using process mining techniques this process can be discovered from the information system’s event logs. A discovered process model of a real user behavior can assist in the analysis of service usability. Thus, for popular and well-designed services this process model will coincide with a reference process model of the expected user behavior. While for other services the observed real behavior and the modeled expected behavior can differ significantly. The main aim of this work is to suggest an approach for the comparison of process models and evaluate its applicability when applied to real-life e-government services.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Gebba, T., Zakaria, M.: E-government in Egypt: An analysis of practices and challenges. Int. J. Bus. Res. Dev. 4(2), 11–25 (2012)

    Google Scholar 

  2. Rorissa, A., Demissie, D.: An analysis of African e-government service websites. Gov. Inf. Quart. 27(2), 161–169 (2010)

    Article  Google Scholar 

  3. Ha, H., Coghill, K.: E-government in Singapore - a SWOT and PEST analysis. Asia Pac. Soc. Sci. Rev. 6(2), 103–130 (2008)

    Article  Google Scholar 

  4. van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  5. Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_27

    Chapter  Google Scholar 

  6. van der Aalst, W.M.P., Weijters, A., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  7. Weijters, A., Ribeiro, J.: Flexible heuristics miner (FHM). In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), Paris, France, IEEE. pp. 310–317, April 2011

    Google Scholar 

  8. van der Aalst, W.M.P., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Günther, C.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87–111 (2010)

    Article  Google Scholar 

  9. Carmona, J., Cortadella, J.: Process mining meets abstract interpretation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 184–199. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15880-3_18

    Chapter  Google Scholar 

  10. Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_24

    Chapter  Google Scholar 

  11. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from incomplete event logs. In: Ciardo, G., Kindler, E. (eds.) PETRI NETS 2014. LNCS, vol. 8489, pp. 91–110. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07734-5_6

    Chapter  Google Scholar 

  12. OMG: Business Process Model and Notation (BPMN). Object Management Group, formal/2013-12-09 (2013)

    Google Scholar 

  13. Kalenkova, A., de Leoni, M., van der Aalst, W.M.P.: Discovering, analyzing and enhancing BPMN models using ProM. In: Proceedings of the BPM Demo Sessions 2014 Co-located with the 12th International Conference on Business Process Management, p. 36 (2014)

    Google Scholar 

  14. Kalenkova, A., van der Aalst, W.M.P., Lomazova, I., Rubin, V.: Process mining using BPMN: relating event logs and process models. Softw. Syst. Model. 16(4), 1019–1048 (2017)

    Article  Google Scholar 

  15. Adriansyah, A., van Dongen, B., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference, EDOC 2011, Washington, DC, USA, pp. 55–64. IEEE Computer Society (2011)

    Google Scholar 

  16. Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Towards robust conformance checking. In: zur Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 122–133. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_11

    Chapter  Google Scholar 

  17. Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. SMC 13(3), 353–362 (1983)

    Article  MATH  Google Scholar 

  18. Ivanov, S., Kalenkova, A., van der Aalst, W.M.P.: BPMNDiffViz: a tool for BPMN models comparison. In: Proceedings of the BPM Demo Session 2015 Co-located with the 13th International Conference on Business Process Management. pp. 35–39 (2015)

    Google Scholar 

  19. Muehlen, M., Recker, J.: How much language is enough? Theoretical and practical use of the business process modeling notation. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 465–479. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69534-9_35

    Chapter  Google Scholar 

  20. Zeng, Z., Tung, A., Wang, J., Feng, J., Zhou, L.: Comparing stars: on approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009)

    Article  Google Scholar 

  21. Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)

    MathSciNet  MATH  Google Scholar 

  22. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. SSC 4(2), 100–107 (1968)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Basic Research Program at the National Research University Higher School of Economics and funded by RFBR and Moscow city Government according to the Research project No 15-37-70008 “mol_a_mos”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. A. Kalenkova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kalenkova, A.A., Ageev, A.A., Lomazova, I.A., van der Aalst, W.M.P. (2018). E-Government Services: Comparing Real and Expected User Behavior. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74030-0_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74029-4

  • Online ISBN: 978-3-319-74030-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics