Method and apparatus for operating an automation system and accounting for concept drift

An apparatus for operating an automation system comprising:

• a) means for obtaining a learning-based prediction model for the automation system trained by process data comprising context of an automation process,
• b) means for receiving information about current context of the automation process,
• c) means for verifying context change by comparing the current context to the context of said process data,
• d) means for verifying a concept drift by comparing pre-drift process data and post-drift process data in the case of any context change,
• e) means for providing post-drift process data for re-training said model in the case of any concept drift,
• f) means for testing for random concept drift not detected by verifying context change in the case of no context change,
• g) means for extending the current context by using data comprising previous context changes in the case of any random concept drift, wherein the current context is automatically extended on the basis of the context's influence on estimates of processing times, the influence being due to at least one dependency in the automation process,
• h) wherein, in the case of no concept drift and no random concept drift, the provided learning-based prediction model is used.