amounts of data stored i make it more more difsicult to get summarized and provided in a timely manner In order to achieve this goal, a fuzzy trigger model is proposed. The model is based on the concept of afuzzy event. Fuzzy events are integrated with fuzzy conditionactions using a new membership function modification technique called squeezing. In addition, this paper describes an application of fuzzy trigger treatmnt in Q real-life industrial drive con The developed model can be applied to g fuzziness with
Int;roduction' h e evkr-increasing amounts of data stored in databases, referred to as data explosion, creates a need to transform
Traditional Event-Condition-Action triggers (active database rules) include a Boolean predicate as a trigger condition. We propose fuzzy triggers whereby fuzzy inference is utilized in the condition evaluation. This way, approximate reasoning may be integrated with a traditional crisp database. The new approach paves the way for intuitive expression of application semantics of imprecise nature, in database-bound applications. Two fuzzy triggers models are proposed. Firstly, a set of fuzzy rules is encapsulated into a Boolean-valued function called a rule set function, leading to the C-fuzzy trigger model. Subsequently, actions are expressed also in fuzzy terms, and the corresponding CA-fuzzy trigger model is proposed. Examples are provided to illustrate how fuzzy triggers can be applied to a real-life drive control system in an industrial installation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.