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. An apparatus for updating a process model, comprising: a deviation module configured to detect deviations between an event log and the process model, the deviation module comprising: a converting module configured to convert 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; a detecting module configured to detect differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and an identifying module configured to, in response to the differences exceeding a predefined threshold, identify the differences as the deviations; 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; and an updating module configured to update the process model based on the deviations. 2. The apparatus according to claim 1, wherein a non-leaf node among the multiple nodes corresponds to a phase in the process model. 3. The apparatus according to claim 2, wherein the converting module comprises: a forming module configured to calculate 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 apparatus according to claim 3, wherein the forming module comprises: a first forming module configured to set the start probabilities, the transition probabilities and the emission probabilities to form the probability process model; and a second forming module configured to iteratively train the probability process model. 5. The apparatus according to claim 1, wherein the detecting module comprises: an aligning module configured to, with respect to a current event sequence of the at least one event sequence, align each event in the current event sequence with the leaf node in the probability process model based on the probability distribution; a path generating module configured to record 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 a determining module configured to determine the differences based on the probability process model and the path sequence. 6. The apparatus according to claim 5, wherein the path comprises a multi-level path corresponding to multiple hierarchies in the probability process model. 7. The apparatus according to claim 5, wherein types of the differences comprise violated constraints, and the determining module comprises: a constructing module configured to use linear temporal logic to construct an automaton conforming to constraints in the probability process model; and an obtaining module configured to obtain the violated constraints from a path sequence that cannot be accepted by the automaton. 8. The apparatus according to claim 5, wherein types of the differences comprise absent activities, and the determining module comprises: a calculating module configured to calculate 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; an absent activities obtaining module configured to calculate a difference set SAbsent=SModel−SLog between the universal set SModel and the universal set SLog as absent activities. 9. The apparatus according to claim 5, wherein types of the differences comprise additional activities, and the determining module comprises: a calculating module configured to calculate 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; an additional activities obtaining module configured to calculate a difference set SAdditional=SLog−SModelbetween the universal set SLog and the universal set SModel as additional activities. 10. The apparatus according to claim 1, wherein the identifying module comprises: a ratio calculating module configured to calculate 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 a deviation identifying module configured to, in response to the ratio exceeding a predefined ratio, identify differences of the type as the deviations.