Marco Di Nepi
A Data Driven Approach to Remaining Time Prediction of Process Instances.
Rel. Silvia Anna Chiusano. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
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Abstract
Large companies usually keep track of internal processes by continuously updating data in a database in the form of logs. They are crucial to carry out conformance checks and monitor whether a case is progressing as expected and similarly to what has happened in the past or, in alternative, detect any errors or unexpected loops that can negatively affect the performances of a system. Predictive process monitoring collects a set of techniques and methodologies to analyze event logs, with the purpose of making predictions on running cases. Being able to predict in real time the remaining time until the completion of a case is crucial to allow the user to intervene promptly.
A fast response guarantees a reduction in the risk of delays and slowdowns in the entire workflow, which may occur in any moment, and an increased awareness on the presence of behaviors that differ from the normal trend
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