Janos Abonyi
Janos Abonyi is a full professor at the Department of Process Engineering at the University of Pannonia in computer science and chemical engineering. He received MEng and PhD degrees in chemical engineering in 1997 and 2000 from the University of Veszprem, Hungary. In 2008, he earned his Habilitation in the field of Process Engineering, and the DSc degree from the Hungarian Academy of Sciences in 2011. In the period of 1999-2000 he was employed at the Control Laboratory of the Delft University of Technology (in the Netherlands). Dr. Abonyi has co-authored more than 250 journal papers and chapters in books and has published five research monographs and one Hungarian textbook about data mining. His research interests include complexity, process engineering, quality engineering, data mining and business process redesign.
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Papers by Janos Abonyi
• The layout-based error detection is determined with the indoor positioning system measurement error.
• This article shows how redundant measurements and data reconciliation can improve the accuracy of such systems.
• Improving the accuracy of position data with the layout-based error map using a data reconciliation algorithm.
• The study presents a unique methodology that integrates sequential rule mining and survival analysis.
• The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions.
• The application of the method is demonstrated within the healthcare domain.
• The layout-based error detection is determined with the indoor positioning system measurement error.
• This article shows how redundant measurements and data reconciliation can improve the accuracy of such systems.
• Improving the accuracy of position data with the layout-based error map using a data reconciliation algorithm.
• The study presents a unique methodology that integrates sequential rule mining and survival analysis.
• The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions.
• The application of the method is demonstrated within the healthcare domain.
today’s productions systems, thousands of alarms are generated
every day in the more and more complex process control units.
The core concept of our work is the investigation of the application possibilities of the Process Mining Framework (PROM) for
the mining of industrial alarm management databases. The taskoriented formalisation of input datasets in the form of XES files
is presented. The applicability and effectiveness of the different
plug-ins are shown in terms of the analysis of the alarm and
event-log database of an industrial delayed-coker plant. The
results illustrate that the presented tools are suitable for the
tracking of activities at different levels of the hierarchy, for the
detection of the spillover effect of different malfunctions and the
detection of the changes in the operating conditions.