I am recently getting back to the time in which I have dealt with semantic data models, and now I am trying to view current event-driven applications in that way, thus the semantic overloading is one of the interesting first issues that emerge. I'll write more about semantic modeling of event processing later, but right now I'll concentrate in the semantic overloading of derived events. There are various definitions of the term "event", but in all of them event represents a VERB in the natural language. Looking at what we defined as derived events, it seems that some of the derived events we are looking at can indeed be described by a verb in the natural language, while others are really described by nouns. Thus my current thinking is to have the semantic notion of DERIVATION, but the derivation can yield different concepts:
Events - when indeed the derived conclusion is that something (virtually) happened.
Entity facts - when the derived conclusion is a value of some fact
Messages - when the derived conclusion is some observation that has to be notified to some actor.
Examples from the Fast Flower Delivery use case that we used in the EPIA book.
The automatic assignment creates a real event -- can be expressed by the verb ASSIGN
The timeout pattern "pickup alert" which means that a pickup was not done on time --- this is an observation that is notified to somebody. It is therefore a message that can be expressed by NOTIFICATION
The driver-ranking calculated as a function of assignment count, is actually a fact related to driver, driver-ranking is a noun, thus it is a derived fact.
More - later.