1 code implementation • 29 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.
5 code implementations • 7 Dec 2016 • Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Multivariate Time Series Forecasting Predictive Process Monitoring +1
1 code implementation • 21 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.
no code implementations • 12 Dec 2017 • Niek Tax, Marlon Dumas
Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database.
1 code implementation • 12 Dec 2017 • Irene Teinemaa, Marlon Dumas, Anna Leontjeva, Fabrizio Maria Maggi
We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability.
2 code implementations • 23 Mar 2018 • Irene Teinemaa, Niek Tax, Massimiliano de Leoni, Marlon Dumas, Fabrizio Maria Maggi
Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof.
no code implementations • 8 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.
no code implementations • 8 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.
2 code implementations • 10 Jul 2018 • Orlenys López-Pintado, Luciano García-Bañuelos, Marlon Dumas, Ingo Weber, Alex Ponomarev
The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN-to-Solidity compiler.
Software Engineering
no code implementations • 31 Jul 2018 • Diego Calvanese, Marlon Dumas, Fabrizio Maria Maggi, Marco Montali
The Decision Model and Notation (DMN) is a recent OMG standard for the elicitation and representation of decision models, and for managing their interconnection with business processes.
2 code implementations • 7 Dec 2018 • Orlenys López-Pintado, Marlon Dumas, Luciano García-Bañuelos, Ingo Weber
The paper also outlines an approach to compile policy specifications into smart contracts for enforcement.
Software Engineering
1 code implementation • 23 May 2019 • Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.
1 code implementation • 4 Jun 2019 • Orlenys López-Pintado, Marlon Dumas, Luciano García-Bañuelos, Ingo Weber
To address this lack of flexibility, this paper presents an interpreter of BPMN process models based on dynamic data structures.
Software Engineering
no code implementations • 11 Oct 2019 • Manuel Camargo, Marlon Dumas, Oscar González-Rojas
This article presents an accuracy-optimized method to discover business process simulation models from execution logs.
no code implementations • 22 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
1 code implementation • 4 Dec 2019 • Gamal Elkoumy, Stephan A. Fahrenkrog-Petersen, Marlon Dumas, Peeter Laud, Alisa Pankova, Matthias Weildich
In this setting, this paper proposes an approach for constructing and querying a common type of artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other.
Cryptography and Security
no code implementations • 3 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.
no code implementations • 7 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.
1 code implementation • 3 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.
1 code implementation • 8 Sep 2020 • Manuel Camargo, Marlon Dumas, Oscar Gonzalez-Rojas
Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models.
1 code implementation • 22 Mar 2021 • Manuel Camargo, Marlon Dumas, Oscar González-Rojas
Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes.
1 code implementation • 15 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.
1 code implementation • 7 Sep 2021 • Mahmoud Shoush, Marlon Dumas
This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints.
no code implementations • 3 Dec 2021 • Kateryna Kubrak, Fredrik Milani, Alexander Nolte, Marlon Dumas
The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.
no code implementations • 30 Jan 2022 • Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, Marco Montali, Jana-Rebecca Rehse, Rafael Accorsi, Diego Calvanese, Giuseppe De Giacomo, Dirk Fahland, Avigdor Gal, Marcello La Rosa, Hagen Völzer, Ingo Weber
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology.
1 code implementation • 15 Jun 2022 • Mahmoud Shoush, Marlon Dumas
Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance.
no code implementations • 28 Jun 2022 • David Chapela-Campa, Marlon Dumas
Business Process Simulation (BPS) is a common approach to estimate the impact of changes to a business process on its performance measures.
1 code implementation • 7 Dec 2022 • Mahmoud Shoush, Marlon Dumas
Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e. g., offering a discount to a customer) to increase the probability of a desired case outcome (e. g., a customer making a purchase).
1 code implementation • 7 Mar 2023 • Zahra Dasht Bozorgi, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush, Irene Teinemaa
Increasing the success rate of a process, i. e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal.
no code implementations • 30 Mar 2023 • David Chapela-Campa, Ismail Benchekroun, Opher Baron, Marlon Dumas, Dmitry Krass, Arik Senderovich
its ability to replicate the observed behavior of the process.
1 code implementation • 13 Jul 2023 • Mahmoud Shoush, Marlon Dumas
This paper argues that, in the presence of resource constraints, a key dilemma in the field of prescriptive process monitoring is to trigger interventions based not only on predictions of their necessity, timeliness, or effect but also on the uncertainty of these predictions and the level of resource utilization.