activity
20182021
most citedDetecting sudden and gradual drifts in business processes from execution traces

85 citations · 85 across the 2 of their papers we have counts for

collaborators

7 papers

cs.SE2021

Discovering executable routine specifications from user interaction logs

Volodymyr Leno, Adriano Augusto, Marlon Dumas +3

Robotic Process Automation (RPA) is a technology to automate routine work such as copying data across applications or filling in document templates using data from multiple applica…

cs.LG2020

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

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-en…

cs.AI202085 cited

Detecting sudden and gradual drifts in business processes from execution traces

Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa +1

Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, se…

cs.LG2019

Business Process Variant Analysis based on Mutual Fingerprints of Event Logs

Farbod Taymouri, Marcello La Rosa, Josep Carmona

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant diffe…

cs.PL2019

Process Query Language: Design, Implementation, and Evaluation

Artem Polyvyanyy, Arthur H. M. ter Hofstede, Marcello La Rosa +2

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models…

cs.AI2018

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

Ilya Verenich, Marlon Dumas, Marcello La Rosa +2

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such…