A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting deviations between an event log and a process model, comprising: converting, with a processing device, the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model. 2. The method according to claim 1, wherein a non-leaf node among the multiple nodes corresponds to a phase in the process model. 3. The method according to claim 2, wherein the converting the process model into a probability process model comprises: calculating start probabilities of occurrence of the non-leaf nodes, transition probabilities among the non-leaf nodes and emission probabilities from the non-leaf nodes to the leaf nodes, so as to form the probability process model. 4. The method according to claim 3, wherein the calculating start probabilities of occurrence of the non-leaf nodes, transition probabilities among the non-leaf nodes and emission probabilities from the non-leaf nodes to the leaf nodes, so as to form the probability process model comprises: setting the start probabilities, the transition probabilities and the emission probabilities to form the probability process model; and iteratively training the probability process model. 5. The method according to claim 1, wherein the detecting differences between at least one event sequence contained in the event log and the probability process model according to the correspondence relationship comprises: with respect to a current event sequence of the at least one event sequence; aligning each event in the current event sequence with the leaf node in the probability process model based on the probability distribution; recording a path, of a leaf node with which each event is aligned, in the probability process model so as to convert the current event sequence into a path sequence; and determining the differences based on the probability process model and the path sequence. 6. The method according to claim 5, wherein the path comprises a multi-level path corresponding to multiple hierarchies in the probability process model. 7. The method according to claim 5, wherein types of the differences comprise violated constraints, and the determining the differences based on the probability process model and the path sequence comprises: using linear temporal logic to construct an automaton conforming to constraints in the probability process model; and obtaining the violated constraints from a path sequence that cannot be accepted by the automaton. 8. The method according to claim 5, wherein types of the differences comprise absent activities, and the determining the differences based on the probability process model and the path sequence comprises: calculating a universal set SModel of paths of the leaf nodes in the probability process model, and a universal set SLog of a path, of a leaf node aligned with an event in an event sequence of the at least one event sequence, in the probability process model, respectively; calculating a difference set SAbsent=SModel−SLog between the universal set SModel and the universal set SLog as absent activities. 9. The method according to claim 5, wherein types of the differences comprise additional activities, and the determining the differences based on the probability process model and the path sequence comprises: calculating a universal set SModel of paths of the leaf nodes in the probability process model, and a universal set SLog of a path, of a leaf node aligned with an event in an event sequence of the at least one event sequence, in the probability process model, respectively; calculating a difference set SAdditional=SLog−SModel between the universal set SLog and the universal set SModel as additional activities. 10. The method according to claim 1, wherein the identifying the differences as the deviations in response to the differences exceeding the predefined threshold comprises: calculating a ratio of the number of event sequences with differences of the at least one event sequence to the total number of the at least one event sequence; and in response to the ratio exceeding a predefined ratio, identifying differences of the type as the deviations.