Skip to main content

Extending Process Logs with Events from Supplementary Sources

  • Conference paper
  • First Online:
Book cover Business Process Management Workshops (BPM 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 202))

Included in the following conference series:

Abstract

Since organizations typically use more than a single IT system, information about the execution of a process is rarely available in a single event log. More commonly, data is scattered across different locations and unlinked by common case identifiers. We present a method to extend an incomplete main event log with events from supplementary data sources, even though the latter lack references to the cases recorded in the main event log. We establish this correlation by using the control-flow, time, resource, and data perspectives of a process model, as well as alignment diagnostics. We evaluate our approach on a real-life event log and discuss the reliability of the correlation under different circumstances. Our evaluation shows that it is possible to correlate a large portion of the events by using our method.

The work of Dr. de Leoni is supported by the Eurostars - Eureka project PROMPT (E!6696).

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

Notes

  1. 1.

    If a transition \(t\) should be associated with no guard, we set \(G(t)=\text {true}\).

  2. 2.

    We use \(\not \rightarrow \) to denote a partial function.

  3. 3.

    The preset of a transition \(t\) is the set of its input places: \({}^{\bullet }t = \{ p \in P \mid (p,t) \in F \}\). The preset of a place \(p\) is the set of its input transitions: \({}^{\bullet }p = \{ t \in T \mid (t,p) \in F \}\).

  4. 4.

    We use \(\mathtt {first}(\sigma )\) to indicate the first element of the sequence \(\sigma \).

  5. 5.

    The replacement operation is represented in the algorithm as function \(\mathtt{replaceMove}(\gamma ,i,move)\) that return a variation of the alignment \(\gamma \) where the \(i\)-th move is replaced by \(move\).

  6. 6.

    \(\mathtt{selectEvent}(C,t)\) denotes the operation of returning the event in a set \(C\) with the closest timestamp to \(t\).

  7. 7.

    http://www.promtools.org.

  8. 8.

    Available at http://dx.doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5.

References

  1. van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    MATH  Google Scholar 

  2. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Technical report, BPM Center Report BPM-14-07 (2014). BPMcenter.org

  3. Claes, J., Poels, G.: Merging computer log files for process mining: an artificial immune system technique. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 99–110. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Nooijen, E.H.J., van Dongen, B.F., Fahland, D.: Automatic discovery of data-centric and artifact-centric processes. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 316–327. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. Motahari-Nezhad, H., Saint-Paul, R., Casati, F., Benatallah, B.: Event correlation for process discovery from web service interaction logs. VLDB J. 20(3), 417–444 (2011)

    Article  Google Scholar 

  6. Walicki, M., Ferreira, D.R.: Sequence partitioning for process mining with unlabeled event logs. Data Knowl. Eng. 70(10), 821–841 (2011)

    Article  Google Scholar 

  7. Zhu, X., Song, S., Wang, J., Yu, P.S., Sun, J.: Matching heterogeneous events with patterns. In: Proceedings of the 2014 30th IEEE International Conference on Data Engineering, ICDE 2014, IEEE, pp. 376–387 (2014)

    Google Scholar 

  8. Desel, J., Esparza, J.: Free Choice Petri Nets. Cambridge University Press, Cambridge (1995)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Felix Mannhardt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Mannhardt, F., de Leoni, M., Reijers, H.A. (2015). Extending Process Logs with Events from Supplementary Sources. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15895-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15894-5

  • Online ISBN: 978-3-319-15895-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics