TY - GEN
T1 - Mining declarative models using time intervals
AU - Werf, van der, J.M.E.M.
AU - Mans, R.S.
AU - Aalst, van der, W.M.P.
PY - 2013
Y1 - 2013
N2 - A common problem in process mining is the interpretation of the time stamp of events, e.g., whether it represents the moment of recording, or its occurrence. Often, this interpretation is left implicit. In this paper, we make this interpretation explicit using time intervals: an event occurs somewhere during a time window. The time window may be fine, e.g., a single point in time, or coarse, like a day. As each event is related to an activity within some process, we obtain for each activity a set of intervals in which the activity occurred. Based on these sets of intervals, we define ordering and simultaneousness relations. These relations form the basis of the discovery of a declarative process model describing the behavior in the event log.
AB - A common problem in process mining is the interpretation of the time stamp of events, e.g., whether it represents the moment of recording, or its occurrence. Often, this interpretation is left implicit. In this paper, we make this interpretation explicit using time intervals: an event occurs somewhere during a time window. The time window may be fine, e.g., a single point in time, or coarse, like a day. As each event is related to an activity within some process, we obtain for each activity a set of intervals in which the activity occurred. Based on these sets of intervals, we define ordering and simultaneousness relations. These relations form the basis of the discovery of a declarative process model describing the behavior in the event log.
M3 - Conference contribution
T3 - CEUR Workshop Proceedings
SP - 313
EP - 331
BT - International Workshop on Modeling and Business Environments (ModBE'13, Milano, Italy, June 24, 2013)
A2 - Moldt, D.
PB - CEUR-WS.org
ER -