Specifically, we define an estimator of the model's generalization based on the event log it was discovered from, and then use bootstrapping to measure the generalization of the model with respect to the system, and its statistical significance.
State-of-the-art process discovery methods construct free-choice process models from event logs.
The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process.
To this end, we develop a technique to forecast the entire process model from historical event data.
Intuitively, the better the quality of the event data, the better the quality of the model that is discovered.
This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome.
no code implementations • 21 Aug 2020 • Artem Polyvyanyy, Hanan Alkhammash, Claudio Di Ciccio, Luciano García-Bañuelos, Anna Kalenkova, Sander J. J. Leemans, Jan Mendling, Alistair Moffat, Matthias Weidlich
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory.
Given an event log as a collection of recorded real-world process traces, process mining aims to automatically construct a process model that is both simple and provides a useful explanation of the traces.
In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another.
The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts.