Search Results for author: Marcello La Rosa

Found 16 papers, 7 papers with code

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.

A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences

1 code implementation15 Feb 2021 Farbod Taymouri, Marcello La Rosa, Sarah M. Erfani

The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions.

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.

Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models

no code implementations30 Jul 2020 Farbod Taymouri, Marcello La Rosa

This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way.

Detecting sudden and gradual drifts in business processes from execution traces

no code implementations7 May 2020 Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, Alireza Ostovar

Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift.

Efficient Conformance Checking using Alignment Computation with Tandem Repeats

no code implementations2 Apr 2020 Daniel Reißner, Abel Armas-Cervantes, Marcello La Rosa

Alignments are an established technique to compute the distance between a trace in the event log and the closest execution trace of a corresponding process model.

Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

1 code implementation25 Mar 2020 Farbod Taymouri, Marcello La Rosa, Sarah Erfani, Zahra Dasht Bozorgi, Ilya Verenich

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp.

Predictive Process Monitoring

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.

Business Process Variant Analysis based on Mutual Fingerprints of Event Logs

no code implementations23 Dec 2019 Farbod Taymouri, Marcello La Rosa, Josep Carmona

The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants.

Scalable Alignment of Process Models and Event Logs: An Approach Based on Automata and S-Components

no code implementations22 Oct 2019 Daniel Reißner, Abel Armas-Cervantes, Raffaele Conforti, Marlon Dumas, Dirk Fahland, Marcello La Rosa

To address this limitation, the paper proposes a second technique wherein the process model is first decomposed into a set of automata, known as S-components, such that the product of these automata is equal to the automaton of the whole process model.

Software Engineering

Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring

no code implementations8 May 2018 Ilya Verenich, Marlon Dumas, Marcello La Rosa, Fabrizio Maggi, Irene Teinemaa

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case.

Discovering Process Maps from Event Streams

no code implementations8 Apr 2018 Volodymyr Leno, Abel Armas-Cervantes, Marlon Dumas, Marcello La Rosa, Fabrizio M. Maggi

Given an event stream produced by the execution of a business process, the goal of an online process discovery method is to maintain a continuously updated model of the process with a bounded amount of memory while at the same time achieving similar accuracy as offline methods.

Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

1 code implementation21 Jul 2017 Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces.

Predictive Process Monitoring

Business Process Deviance Mining: Review and Evaluation

1 code implementation29 Aug 2016 Hoang Nguyen, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Suriadi Suriadi

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes.

General Classification

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