Context-Aware Process Performance Indicator Prediction
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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9293289/ |
id |
doaj-e9f6f331580c4922aa8269252176114a |
---|---|
record_format |
Article |
spelling |
doaj-e9f6f331580c4922aa8269252176114a2021-03-30T04:29:39ZengIEEEIEEE Access2169-35362020-01-01822205022206310.1109/ACCESS.2020.30446709293289Context-Aware Process Performance Indicator PredictionAlfonso E. Marquez-Chamorro0https://orcid.org/0000-0002-8243-0404Kate Revoredo1Manuel Resinas2https://orcid.org/0000-0003-1575-406XAdela Del-Rio-Ortega3https://orcid.org/0000-0003-3089-4431Flavia M. Santoro4https://orcid.org/0000-0003-3421-1984Antonio Ruiz-Cortes5https://orcid.org/0000-0001-9827-1834Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Informatics Engineering (I3US), Universidad de Sevilla, Seville, SpainDepartment of Information Systems and Operations, Vienna University of Economics and Business (WU), Vienna, AustriaSmart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Informatics Engineering (I3US), Universidad de Sevilla, Seville, SpainSmart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Informatics Engineering (I3US), Universidad de Sevilla, Seville, SpainGraduate Program of Informatics, University of the State of Rio de Janeiro, Rio de Janeiro, BrazilSmart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Informatics Engineering (I3US), Universidad de Sevilla, Seville, SpainIt 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.https://ieeexplore.ieee.org/document/9293289/Business process managementprocess miningpredictive monitoringcontext awarenessprocess indicator prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alfonso E. Marquez-Chamorro Kate Revoredo Manuel Resinas Adela Del-Rio-Ortega Flavia M. Santoro Antonio Ruiz-Cortes |
spellingShingle |
Alfonso E. Marquez-Chamorro Kate Revoredo Manuel Resinas Adela Del-Rio-Ortega Flavia M. Santoro Antonio Ruiz-Cortes Context-Aware Process Performance Indicator Prediction IEEE Access Business process management process mining predictive monitoring context awareness process indicator prediction |
author_facet |
Alfonso E. Marquez-Chamorro Kate Revoredo Manuel Resinas Adela Del-Rio-Ortega Flavia M. Santoro Antonio Ruiz-Cortes |
author_sort |
Alfonso E. Marquez-Chamorro |
title |
Context-Aware Process Performance Indicator Prediction |
title_short |
Context-Aware Process Performance Indicator Prediction |
title_full |
Context-Aware Process Performance Indicator Prediction |
title_fullStr |
Context-Aware Process Performance Indicator Prediction |
title_full_unstemmed |
Context-Aware Process Performance Indicator Prediction |
title_sort |
context-aware process performance indicator prediction |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Business process management process mining predictive monitoring context awareness process indicator prediction |
url |
https://ieeexplore.ieee.org/document/9293289/ |
work_keys_str_mv |
AT alfonsoemarquezchamorro contextawareprocessperformanceindicatorprediction AT katerevoredo contextawareprocessperformanceindicatorprediction AT manuelresinas contextawareprocessperformanceindicatorprediction AT adeladelrioortega contextawareprocessperformanceindicatorprediction AT flaviamsantoro contextawareprocessperformanceindicatorprediction AT antonioruizcortes contextawareprocessperformanceindicatorprediction |
_version_ |
1724181822333517824 |