Modeling and detecting high-level events in healthcare applications exploiting ISEQL+

Modeling and automatically detecting complex events in different domains, such as video surveillance and healthcare, is becoming an increasingly topical issue nowadays. In fact, deriving knowledge on higher level from low-level events by combining the latter to complex structures is the task of an Event Query Language (EQL), whose main issue is the lack of formal semantics. Consequently, in order to cope with this issue, in this paper we propose $$ISEQL+$$ , an extension of ISEQL (an Interval-based Surveillance Event Query Language, that we previously defined), aimed at further improving its expressiveness. More specifically, we provide formal proofs demonstrating that the language fully covers the well-known Allen’s interval relationships, additionally supports conditional overlap ratio and conditional cardinality constraints over the interval relationships, provides robustness with respect to small variations in the intervals, and can be formalized as relational algebra extension, which will in turn allow a very efficient implementation exploiting an existing algorithm. Eventually, we also show how typical events in the healthcare domain can be easily expressed via $$ISEQL+$$ .