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Towards Event Log Querying for Data Quality

Let’s Start with Detecting Log Imperfections

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On the Move to Meaningful Internet Systems. OTM 2018 Conferences (OTM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11229))

Abstract

Process mining is, by now, a well-established discipline focussing on process-oriented data analysis. As with other forms of data analysis, the quality and reliability of insights derived through analysis is directly related to the quality of the input (garbage in - garbage out). In the case of process mining, the input is an event log comprised of event data captured (in information systems) during the execution of the process. It is crucial then that the event log be treated as a first-class citizen. While data quality is an easily understood concept little effort has been directed towards systematically detecting data quality issues in event logs. Analysts still spend a large proportion of any project in ‘data cleaning’, often involving manual and ad hoc tasks, and requiring more than one tool. While there are existing tools and languages that query event logs, the problem of different approaches for different log imperfections remains. In this paper we take the first steps to developing QUELI (Querying Event Log for Imperfections) a log query language that provides direct support for detecting log imperfections. We develop an approach that identifies capabilities required of QUELI and illustrate the approach by applying it to 5 of the 11 event log imperfection patterns described in [29]. We view this as a first step towards operationalising systematic, automated support for log cleaning.

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Notes

  1. 1.

    http://www.workflowpatterns.com/patterns/logimperfection/.

References

  1. ISO/IEC 25010:2011: Systems and software engineering - Systems and software product Quality Requirements and Evaluation (SQuaRE) - System and software quality models (2011)

    Google Scholar 

  2. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

  3. van der Aalst, W.: Process Mining: Discovery Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3

    Book  MATH  Google Scholar 

  4. Batini, C., Palmonari, M., Viscusi, G.: Opening the closed world: a survey of information quality research in the wild. In: Floridi, L., Illari, P. (eds.) The Philosophy of Information Quality. SL, vol. 358, pp. 43–73. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07121-3_4

    Chapter  Google Scholar 

  5. Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-33173-5

    Book  MATH  Google Scholar 

  6. Beheshti, S.-M.-R., Benatallah, B., Motahari-Nezhad, H.R.: Scalable graph-based OLAP analytics over process execution data. Distrib. Parallel Datab. 34(3), 379–423 (2016)

    Article  Google Scholar 

  7. Beheshti, S.-M.-R., Benatallah, B., Motahari-Nezhad, H.R., Sakr, S.: A query language for analyzing business processes execution. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 281–297. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23059-2_22

    Chapter  Google Scholar 

  8. Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 159–175. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03848-8_12

    Chapter  Google Scholar 

  9. Jagadeesh Chandra Bose, R.P., Mans, R.S., van der Aalst, W.M.: Wanna improve process mining results? CIDM 2013, 127–134 (2013)

    Google Scholar 

  10. Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31164-2

    Book  Google Scholar 

  11. CrowdFlower: 2017 Data Scientist Report (2017). https://visit.crowdflower.com. Accessed 25 July 2018

  12. Dijkman, R., Gao, J., Grefen, P., ter Hofstede, A.: Relational algebra for in-database process mining. arXiv preprint arXiv:1706.08259 (2017)

  13. Dixit, P.M., et al.: Detection and interactive repair of event ordering imperfection in process logs. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 274–290. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_17

    Chapter  Google Scholar 

  14. Durand, J., Cho, H., Moberg, D., Woo, J.: XTemp: event-driven testing and monitoring of business processes. In: Proceedings of Balisage, The Markup Conference 2011, vol. 7. Balisage Series on Markup Technologies (2011)

    Google Scholar 

  15. Günther, C.W., Rozinat, A.: Disco: discover your processes. BPM (Demos) 940, 40–44 (2012)

    Google Scholar 

  16. Laranjeiro, N., Soydemir, S.N., Bernardino, J.: A survey on data quality: classifying poor data. In: PRDC 2015, pp. 179–188. IEEE (2015)

    Google Scholar 

  17. Leemans, M., van der Aalst, W.M.P.: Discovery of frequent episodes in event logs. In: Ceravolo, P., Russo, B., Accorsi, R. (eds.) SIMPDA 2014. LNBIP, vol. 237, pp. 1–31. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27243-6_1

    Chapter  Google Scholar 

  18. Lohr, S.: For big-data scientists, ‘janitor work’ is key hurdle to insights. New York Times, 17 August 2014

    Google Scholar 

  19. Lu, X., et al.: Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In: Panetto, H., et al. (eds.) OTM On the Move to Meaningful Internet Systems, pp. 154–174. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_11

    Chapter  Google Scholar 

  20. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_8

    Chapter  Google Scholar 

  21. Mans, R.S., van der Aalst, W.M., Vanwersch, R., Moleman, A.: Process Support and Knowledge Representation in Health Care. LNCS, vol. 7738, pp. 140–153. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36438-9

    Book  Google Scholar 

  22. González López de Murillas, E., Reijers, H.A., van der Aalst, W.M.P.: Everything you always wanted to know about your process, but did not know how to ask. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 296–309. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_22

    Chapter  Google Scholar 

  23. Perez-Alvarez, J.M., Gomez-Lopez, M.T., Parody, L., Gasca, R.M.: Process instance query language to include process performance indicators in DMN. In: EDOCW 2016, pp. 1–8. IEEE (2016)

    Google Scholar 

  24. Prud‘hommeaux, E., Seaborne, A.: SPARQL query language for RDF. W3C recommendation, January 2008 (2008)

    Google Scholar 

  25. Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_18

    Chapter  Google Scholar 

  26. Shabani, S., et al.: Relational XES: data management for process mining. In: CAiSE 2015. CEUR-WS. org (2015)

    Google Scholar 

  27. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)

    Article  Google Scholar 

  28. Strong, D.M., Lee, Y.W., Wang, R.Y.: Data quality in context. Commun. ACM 40(5), 103–110 (1997)

    Article  Google Scholar 

  29. Suriadi, S., Andrews, R., ter Hofstede, A., Wynn, M.: Event log imperfection patterns for process mining: towards a systematic approach to cleaning event logs. Inf. Syst. 64, 132–150 (2017)

    Article  Google Scholar 

  30. Suriadi, S., Wynn, M.T., Ouyang, C., ter Hofstede, A.H.M., van Dijk, N.J.: Understanding process behaviours in a large insurance company in australia: a case study. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 449–464. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_29

    Chapter  Google Scholar 

  31. Vázquez-Barreiros, B., Mucientes, M., Lama, M.: Mining duplicate tasks from discovered processes. In: ATAED@ Petri Nets/ACSD, pp. 78–82 (2015)

    Google Scholar 

  32. Verhulst, R.: Evaluating quality of event data within event logs: an extensible framework. Ph.D. thesis, Technische Universiteit Eindhoven (2016)

    Google Scholar 

  33. Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)

    Article  Google Scholar 

  34. Wang, R.Y., Storey, V., Firth, C.: A framework for analysis of data quality research. IEEE Trans. Knowl. Data Eng. 7(4), 623–640 (1995)

    Article  Google Scholar 

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Acknowledgement

The contributions to this paper of Robert Andrews and Chun Ouyang were supported through ARC Discovery Grant DP150103356.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Robert Andrews, Suriadi Suriadi, Chun Ouyang & Erik Poppe

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  1. Robert Andrews
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  2. Suriadi Suriadi
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  3. Chun Ouyang
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  4. Erik Poppe
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Corresponding author

Correspondence to Robert Andrews .

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Editors and Affiliations

  1. CNRS, University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  2. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Henderik A. Proper

  4. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  5. SINTEF and University of Oslo, Oslo, Norway

    Dumitru Roman

  6. TU Graz, Graz, Austria

    Robert Meersman

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Andrews, R., Suriadi, S., Ouyang, C., Poppe, E. (2018). Towards Event Log Querying for Data Quality. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-02610-3_7

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