Comparing Concept Drift Detection with Process Mining Software
نویسندگان
چکیده
منابع مشابه
Concept drift detection in business process logs using deep learning
Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...
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Operational processes need to change to adapt to changing circumstances, e.g., new legislation, extreme variations in supply and demand, seasonal effects, etc. While the topic of flexibility is well-researched in the BPM domain, contemporary process mining approaches assume the process to be in steady state. When discovering a process model from event logs, it is assumed that the process at the...
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Concept drift is an important concern for any data analysis scenario involving temporally ordered data. In the last decade Process mining arose as a discipline that uses the logs of information systems in order to mine, analyze and enhance the process dimension. There is very little work dealing with concept drift in process mining. In this paper we present the first online mechanism for detect...
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Concept drift is the condition when the process changes during the course of execution. Current methods and analysis techniques existing in process mining are not proficient of analyzing the process which has experienced the concept drift. State-of-the-art process mining approaches consider the process as a static entity and assume that process remains same from beginning of its execution perio...
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ژورنال
عنوان ژورنال: iSys - Brazilian Journal of Information Systems
سال: 2020
ISSN: 1984-2902
DOI: 10.5753/isys.2020.832