Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach
Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, tho...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/13/11/267 |
| _version_ | 1797550080170917888 |
|---|---|
| author | Niyi Ogunbiyi Artie Basukoski Thierry Chaussalet |
| author_facet | Niyi Ogunbiyi Artie Basukoski Thierry Chaussalet |
| author_sort | Niyi Ogunbiyi |
| collection | DOAJ |
| description | Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches. |
| first_indexed | 2024-03-10T15:24:23Z |
| format | Article |
| id | doaj.art-2b95817c1da24101b48c95536c862822 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T15:24:23Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-2b95817c1da24101b48c95536c8628222023-11-20T18:09:05ZengMDPI AGAlgorithms1999-48932020-10-01131126710.3390/a13110267Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis ApproachNiyi Ogunbiyi0Artie Basukoski1Thierry Chaussalet2School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKPredictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches.https://www.mdpi.com/1999-4893/13/11/267operational business process managementpredictive process monitoringremaining time predictive modellingsocial context |
| spellingShingle | Niyi Ogunbiyi Artie Basukoski Thierry Chaussalet Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach Algorithms operational business process management predictive process monitoring remaining time predictive modelling social context |
| title | Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach |
| title_full | Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach |
| title_fullStr | Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach |
| title_full_unstemmed | Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach |
| title_short | Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach |
| title_sort | investigating social contextual factors in remaining time predictive process monitoring a survival analysis approach |
| topic | operational business process management predictive process monitoring remaining time predictive modelling social context |
| url | https://www.mdpi.com/1999-4893/13/11/267 |
| work_keys_str_mv | AT niyiogunbiyi investigatingsocialcontextualfactorsinremainingtimepredictiveprocessmonitoringasurvivalanalysisapproach AT artiebasukoski investigatingsocialcontextualfactorsinremainingtimepredictiveprocessmonitoringasurvivalanalysisapproach AT thierrychaussalet investigatingsocialcontextualfactorsinremainingtimepredictiveprocessmonitoringasurvivalanalysisapproach |