Yeshchenko, Anton, Mendling, Jan, Di Ciccio, Claudio, Polyvyanny, Artem. 2020. VDD: A Visual Drift Detection System for Process Mining. In CEUR Workshop Proceedings, Hrsg. Massimiliano de Leoni, University of Padua (Italy) Alessandro Sperduti, University of Padua (Italy), 31-34. Padua (Italy): None.

RIS

@INPROCEEDINGS{Yeshchenko2020,
title = {VDD: A Visual Drift Detection System for Process Mining},
author = {Anton Yeshchenko and Jan Mendling and Claudio Di Ciccio and Artem Polyvyanny},
publisher = {None},
year = {2020},
address = {Padua (Italy)},
url = {https://www.semanticscholar.org/paper/VDD%3A-A-Visual-Drift-Detection-System-for-Process-Yeshchenko-Mendling/ce7ecc593803816dc155618fc037e353cc317116},
language = {EN},
pages = {31-34},
series = {CEUR Workshop Proceedings},
abstract = {Research on concept drift detection has inspired recent advancements of process mining and expanding the growing arsenal of process analysis tools. What has so far been missing in this new research stream are techniques that support comprehensive process drift analysis in terms of localizing, drillingdown, quantifying, and visualizing process drifts. In our research, we built on ideas from concept drift, process mining, and visualization research and present a novel web-based software tool to analyze process drifts, called Visual Drift Detection (VDD). Addressing the comprehensive analysis requirements, our tool is of benefit to researchers and practitioners in the business intelligence and process analytics area. It constitutes a valuable aid to those who are involved in business process redesign projects},
}