Search Results for author: Artem Polyvyanyy

Found 10 papers, 6 papers with code

Bootstrapping Generalization of Process Models Discovered From Event Data

1 code implementation8 Jul 2021 Artem Polyvyanyy, Alistair Moffat, Luciano García-Bañuelos

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.

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

1 code implementation15 May 2021 Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy

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.

Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs

1 code implementation3 Sep 2020 Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy

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.

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

no code implementations21 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.

An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining

no code implementations18 Jul 2020 Artem Polyvyanyy, Alistair Moffat, Luciano García-Bañuelos

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.

Automated Discovery of Data Transformations for Robotic Process Automation

no code implementations3 Jan 2020 Volodymyr Leno, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Artem Polyvyanyy

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.

Comprehensive Process Drift Detection with Visual Analytics

1 code implementation15 Jul 2019 Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, Artem Polyvyanyy

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.

Change Point Detection

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