Context-Aware Process Performance Indicator Prediction

Marquez-Chamorro, Alfonso ORCID: https://orcid.org/0000-0002-8243-0404 and Cerqueira Revoredo, Kate ORCID: https://orcid.org/0000-0001-8914-9132 and Resinas, Manuel ORCID: https://orcid.org/0000-0002-8243-0404 and Del-Rio-Ortega, Adela ORCID: https://orcid.org/0000-0003-3089-4431 and Santoro, Flavia ORCID: https://orcid.org/0000-0003-3421-1984 and Ruiz-Cortes, Antonio ORCID: https://orcid.org/0000-0001-9827-1834 (2020) Context-Aware Process Performance Indicator Prediction. IEEE Access, 8. pp. 222050-222063. ISSN 2169-3536

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Abstract

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phase guides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.

Item Type: Article
Additional Information: This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under Grant 645751 RISE_BPM, and in part by the Spanish Government through projects HORATIO and OPHELIA under Grant RTI2018-101204-B-C21/C22. The work of Kate Revoredo was supported by the Osterreichische Akademie der Wissenschaften.
Divisions: Departments > Wirtschaftsinformatik u. Operations Mgmt > Data, Process and Knowledge Management > Informationswirtschaft
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 11 Jan 2021 10:03
Last Modified: 11 Jan 2021 10:03
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/98374/
URI: https://epub.wu.ac.at/id/eprint/7935

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