Atherosclerosis is an inflammatory disease with fundamental primitive events including endothelial dysfunction and platelet aggregation [1].
That is, a system only capable of dealing with primitive events is far more limited than one employing composite events[52] or other context modelling techniques[42] that can correlate and compose events within and across message streams.
The proposed framework is capable of handling relationships between primitive events generated from (1) a single camera view, (2) multiple camera views, and (3) nonvideo sensors with spatial and temporal variations.
Complex event processing (CEP) engines in charge of interpreting, filtering, and combining primitive events to identify higher level composite events according to a set of rules are the new breed of message-oriented middleware, which is being proposed today to better support event-driven interactions.
A reckless maneuver (e.g., rapidly turning at a high speed) is an example of a complex event, in so far as it is based on the composition of primitive events, such as acceleration, speed, and wheel direction.
In event correlation [15], ensembles of primitive events are combined to form composite events [16].
One prominent version of adverbialism is the event modification theory, which quantifies over primitive events and takes adverbs to modify these events — see Davidson (1980).
CEP systems were developed in parallel to DSMSs and represent a different approach towards the analysis of streaming data, which targets the detection of situations of interest from patterns of primitive events [14, 15].
Composite events are represented in the form of full binary trees, where the leaf nodes represent primitive events, the root node represents the target composite event, and the middle nodes represent rule definitions.
