Search Results for author: Marlon Dumas

Found 32 papers, 20 papers with code

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

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

Mining Non-Redundant Local Process Models From Sequence Databases

no code implementations12 Dec 2017 Niek Tax, Marlon Dumas

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database.

Sequential Pattern Mining

Temporal Stability in Predictive Process Monitoring

1 code implementation12 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.

Binary Classification Predictive Process Monitoring +2

Alarm-Based Prescriptive Process Monitoring

2 code implementations23 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.

Predictive Process Monitoring

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.

Management

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.

CATERPILLAR: A Business Process Execution Engine on the Ethereum Blockchain

2 code implementations10 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

Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

no code implementations31 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.

Dynamic Role Binding in Blockchain-Based Collaborative Business Processes

2 code implementations7 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

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

1 code implementation23 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.

Predictive Process Monitoring

Interpreted Execution of Business Process Models on Blockchain

1 code implementation4 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

Automated Discovery of Business Process Simulation Models from Event Logs

no code implementations11 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.

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

Secure Multi-Party Computation for Inter-Organizational Process Mining

1 code implementation4 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

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.

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.

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.

BIG-bench Machine Learning

Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning

1 code implementation8 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.

Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning

1 code implementation22 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.

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.

Management

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

1 code implementation7 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.

Causal Inference

Prescriptive Process Monitoring: Quo Vadis?

no code implementations3 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.

When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

1 code implementation15 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.

Enhancing Business Process Simulation Models with Extraneous Activity Delays

no code implementations28 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.

Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes

1 code implementation7 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).

Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach

1 code implementation13 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.

Conformal Prediction reinforcement-learning

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