Abstract
An approach to the development of a digital ecosystem (DES) which comprises interacting service ssubsystems (consorts) is presented. Methods for analyzing and prediction of the production situations using machine learning algorithms and inductive knowledge bases are proposed. The possibility of using these methods for predicting the state of the digital infrastructure resources of industrial enterprises in real time is demonstrated. The paper shows that the methods of proactive decision support for situational control can help production companies to prevent abnormal situations. Case studies are included to illustrate the creation of association rules in the state prediction problem for an enterprise resource complex.
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