ECA rule learning in dynamic environments

• Design of an algorithm to ECA rule learning in dynamic environments.
• Presented is a hierarchical approach to rule learning.
• Learning the rules for online streaming data.
• Development of multi flexible decision tree to support multi-part divisions.

Through the development of management and intelligent control systems, we can make useful decision by using incoming data. These systems are used commonly in dynamic environments that some of which are been rule-based architectures. Event–Condition–Action (ECA) rule is one of the types that are used in dynamic environments. ECA rules have been designed for the systems that need automatic response to certain conditions or events. Changes of environmental conditions during the time are important factors impacting a reduction of the effectiveness of these rules which are implied by changing users demands of the systems that vary over time. Also, the rate of the changes in the rules are not known which means we are faced with the lack of information about rate of occurrence of new unknown conditions as a result of dynamics environments. Therefore, an intelligent rule learning is required for ECA rules to maintain the efficiency of the system. To the best knowledge of the authors, ECA rule learning has not been investigated. An intelligent rule learning for ECA rules are studied in this paper and a method is presented by using a combination of multi flexible fuzzy tree (MFlexDT) algorithm and neural network. Hence data loss could be avoided by considering the uncertainty aspect. Owing to runtime, speed, and also stream data in dynamic environments, a hierarchical learning model is proposed. We evaluate the performance of the proposed method for resource management in the Grid and e-commerce as case studies by modeling and simulating. A case study is presented to show the applicability of the proposed method.