Business process improvement ideas can be validated through sequential experiment techniques like AB Testing. Such approaches have the inherent risk of exposing customers to an inferior process version, which is why the inferior version should be discarded as quickly as possible. In this paper, we propose a contextual multi-armed bandit algorithm that can observe the performance of process versions and dynamically adjust the routing policy so that the customers are directed to the version that can best serve them. Our algorithm learns the best routing policy in the presence of complications such as multiple process performance indicators, delays in indicator observation, incomplete or partial observations, and contextual factors. We also propose a pluggable architecture that supports such routing algorithms. We evaluate our approach with a case study. Furthermore, we demonstrate that our approach identifies the best routing policy given the process performance and that it scales horizontally.

AB testing for process versions with contextual multi-armed bandit algorithms / Suhrid, Satyal; Ingo, Weber; Hye-young, Paik; Di Ciccio, C; Jan, Mendling:. - (2018), pp. 19-34. (Intervento presentato al convegno Advanced Information Systems Engineering - 30th International Conference (CAISE 2018) tenutosi a Tallin; Estonia) [10.1007/978-3-319-91563-0_2].

AB testing for process versions with contextual multi-armed bandit algorithms

Di Ciccio C;
2018

Abstract

Business process improvement ideas can be validated through sequential experiment techniques like AB Testing. Such approaches have the inherent risk of exposing customers to an inferior process version, which is why the inferior version should be discarded as quickly as possible. In this paper, we propose a contextual multi-armed bandit algorithm that can observe the performance of process versions and dynamically adjust the routing policy so that the customers are directed to the version that can best serve them. Our algorithm learns the best routing policy in the presence of complications such as multiple process performance indicators, delays in indicator observation, incomplete or partial observations, and contextual factors. We also propose a pluggable architecture that supports such routing algorithms. We evaluate our approach with a case study. Furthermore, we demonstrate that our approach identifies the best routing policy given the process performance and that it scales horizontally.
2018
Advanced Information Systems Engineering - 30th International Conference (CAISE 2018)
AB testing; business process management; multi-armed bandit; process performance indicators
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
AB testing for process versions with contextual multi-armed bandit algorithms / Suhrid, Satyal; Ingo, Weber; Hye-young, Paik; Di Ciccio, C; Jan, Mendling:. - (2018), pp. 19-34. (Intervento presentato al convegno Advanced Information Systems Engineering - 30th International Conference (CAISE 2018) tenutosi a Tallin; Estonia) [10.1007/978-3-319-91563-0_2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1372941
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