Abstract: Data events of an event stream are processed in accordance with temporally valid machine learning models. A streaming node may receive data events via an event stream. Each data event may be associated with a timestamp. The streaming node may also utilize punctuation events that specify the temporal validity of available machine learning models. The streaming node performs a temporal join operation for each data event based on its timestamp and the temporal validity. If the data event's timestamp is less than or equal to the punctuation event's timestamp, the data event is provided to the temporally valid machine learning model for processing thereby. If the data event's timestamp is greater than the punctuation event's timestamp, the data event is held until a subsequent punctuation event specifying a later timestamp is received.

Abstract: Data events of an event stream are processed in accordance with temporally valid machine learning models. A streaming node may receive data events via an event stream. Each data event may be associated with a timestamp. The streaming node may also utilize punctuation events that specify the temporal validity of available machine learning models. The streaming node performs a temporal join operation for each data event based on its timestamp and the temporal validity. If the data event's timestamp is less than or equal to the punctuation event's timestamp, the data event is provided to the temporally valid machine learning model for processing thereby. If the data event's timestamp is greater than the punctuation event's timestamp, the data event is held until a subsequent punctuation event specifying a later timestamp is received.