An associated event group preparation unit calculates a degree of association between the events on the basis of an event matrix, and prepares an associated event group for each event. A cause-and-effect relationship model establishment unit establishes a probabilistic cause-and-effect relationship model by a Bayesian network on the basis of the event matrix, for each associated event group. An improvement candidate pattern receiving unit receives a setting of an improvement candidate pattern in which a condition of an event to be set as an improvement candidate is determined by attributes of the base point event and the associated event and a conditional probability between the base point event and the associated event. A pattern analysis unit extracts a probabilistic cause-and-effect relationship model conforming to any one of the set improvement candidate patterns, from the established probabilistic cause-and-effect relationship models for each event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An event analysis apparatus configured to analyze events that occur in a plant, the apparatus comprising: an associated event group preparation unit configured to calculate a degree of association between the events on the basis of an event matrix, which represents presence and absence of occurrence of each event including an attribute indicating an alarm or an operation in time series, and to prepare an associated event group for each event, in which a specific event is set as a base point event and the base point event and an event having a high degree of association are grouped; a cause-and-effect relationship model establishment unit configured to establish a probabilistic cause-and-effect relationship model by a Bayesian network on the basis of the event matrix, for each associated event group; an improvement candidate pattern receiving unit configured to receive a setting of an improvement candidate pattern in which a condition of an event to be set as an improvement candidate is determined by attributes of the base point event and the associated event and a conditional probability between the base point event and the associated event; and a pattern analysis unit configured to extract a probabilistic cause-and-effect relationship model conforming to any one of the set improvement candidate patterns, from the established probabilistic cause-and-effect relationship models for each event. 2. The event analysis apparatus according to claim 1, wherein the associated event group preparation unit is configured to divide the event matrix into blocks having a predetermined reference time width, to calculate an individual occurrence probability of the events and a simultaneous occurrence probability between the events, on the basis of presence and absence of occurrence of each event in each block, and to use a mutual information amount, which is to be obtained from the individual occurrence probability and the simultaneous occurrence probability, as the degree of association between the events. 3. The event analysis apparatus according to claim 1, wherein the improvement candidate pattern is a pattern in which the associated event is distinguished into a cause-side event and an effect-side event in relation to the base point event, and an attribute and a condition of a conditional probability are determined for each of the cause-side event and the effect-side event. 4. The event analysis apparatus according to claim 1, wherein the improvement candidate pattern is set a condition that there is no associated event. 5. The event analysis apparatus according to claim 1, wherein the improvement candidate pattern is set that a condition is not set for an associated event. 6. An event analysis method of analyzing events occurring in a plant, the method comprising: calculating a degree of association between the events on the basis of an event matrix, which represents presence and absence of occurrence of each event including an attribute indicating an alarm or an operation in time series, and preparing an associated event group for each event, in which a specific event is set as a base point event and the base point event and an event having a high degree of association are grouped; establishing a probabilistic cause-and-effect relationship model by a Bayesian network on the basis of the event matrix, for each associated event group; receiving a setting of an improvement candidate pattern in which a condition of an event to be set as an improvement candidate is determined by attributes of the base point event and the associated event and a conditional probability between the base point event and the associated event; and extracting a probabilistic cause-and-effect relationship model conforming to any one of the set improvement candidate patterns, from the established probabilistic cause-and-effect relationship models for each event. 7. A computer program product, comprising: a non-transitory computer-readable medium comprising code for causing an information processing apparatus to: calculate a degree of association between the events on the basis of an event matrix, which represents presence and absence of occurrence of each event including an attribute indicating an alarm or an operation in time series, and to prepare an associated event group for each event, in which a specific event is set as a base point event and the base point event and an event having a high degree of association are grouped; establish a probabilistic cause-and-effect relationship model by a Bayesian network on the basis of the event matrix, for each associated event group; receive a setting of an improvement candidate pattern in which a condition of an event to be set as an improvement candidate is determined by attributes of the base point event and the associated event and a conditional probability between the base point event and the associated event; and extract a probabilistic cause-and-effect relationship model conforming to any one of the set improvement candidate patterns, from the established probabilistic cause-and-effect relationship models for each event.