eetpatterns

eetpatterns is a term used in data science to describe patterns in sequences of events where the order of events and the timing between them are important. An eetpattern consists of a sequence of event types E1, E2, ..., Ek and a corresponding set of time gaps Δ1, ..., Δ(k-1). A match occurs when observed events follow the specified order and the inter-event times fall within predefined tolerance windows. Patterns are typically characterized by the degree of repetition (support) and by statistics on the inter-event gaps.

Formally, an eetpattern is defined by (E1, E2, ..., Ek; Δ1, ..., Δ(k-1)) and a tolerance function that specifies acceptable deviations for each gap. In practice, different variants exist, including patterns with fixed-length gaps, flexible gaps, or probabilistic timing. The concept is closely related to sequential pattern mining but with an explicit emphasis on temporal structure.

Detection and analysis of eetpatterns employ a range of methods, such as time-constrained frequent pattern mining, sequence alignment, and probabilistic sequence models that incorporate temporal constraints. Techniques may seek maximal or closed patterns, and may incorporate noise handling, missing data, and online or streaming updates for real-time applications.

Applications of eetpatterns span several domains. In cybersecurity and system monitoring, they help identify attack or failure chains in log data. In healthcare, they model progression sequences of symptoms or treatments. In industrial settings, they reveal sequences of sensor events signaling normal or anomalous operation. In marketing and web analytics, they capture typical user navigation or interaction flows.

See also: sequential pattern mining, time-series data mining, process mining.