Abstract: An information processing apparatus identifies a state of a specific user with respect to an object among a plurality of persons included in video data by analyzing the video data obtained by capturing a first area including a person or the object, generates a questionnaire related to the person or the object, identifies positions and orientations of users among the plurality of persons with respect to a display device by analyzing video data obtained by capturing a second area including the display device, and displays the questionnaire related to the specific user on the display device when the specific user is in a state of being closest to the display device and facing the display device and another user is away from the specific user and from the display device and does not face the display device.

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes acquiring video data that has target objects that include a person and an object; specifying each relationship of each of the target objects in the acquired video data by using graph data that indicates a relationship of each of target object stored in a storage unit; specifying a behavior of the person in the acquired video data by using a feature of the person; and predicting a future behavior of the person or a state of the person by comparing the specified behavior of the person and the specified relationship with a behavior prediction rule, the behavior prediction rule being a rule associated with a future behavior of a person for each combination of a behavior of the person and a relationship.

Abstract: A movement history change method for a computer to execute a process includes acquiring identification information of a first object detected at a first location by a sensor and a detection timing of the first object from object detection history information that indicates time-series of detection; identifying identification information of a second object present at the first location at a first timing corresponding to the detection timing in the object movement history information generated from a video; and changing the identification information of the second object to the identification information of the first object in the object movement history information.

Abstract: In order to identify a parallel-executed portion of processes included in a business flow, following stages are executed: obtaining transaction records including a processing time, a process type and an identifier from a database; grouping the transaction records based on the identifier and sorting the transaction records within a group based on the processing time; in case where the processing time included in the transaction record represents only one time, calculating a processing period of each transaction record based on a sequence of the processing times in the transaction records in the group; identifying a transaction record relating to a process executed in parallel based on the distribution of the calculated processing periods for each process type; and identifying, for each group, a business flow of the group based on the processing result of the identifying the transaction record and the transaction records in the group.

Abstract: In order to identify a parallel-executed portion of processes included in a business flow, following stages are executed: obtaining transaction records including a processing time, a process type and an identifier from a database; grouping the transaction records based on the identifier and sorting the transaction records within a group based on the processing time; in case where the processing time included in the transaction record represents only one time, calculating a processing period of each transaction record based on a sequence of the processing times in the transaction records in the group; identifying a transaction record relating to a process executed in parallel based on the distribution of the calculated processing periods for each process type; and identifying, for each group, a business flow of the group based on the processing result of the identifying the transaction record and the transaction records in the group.

Abstract: A series of transaction records is extracted for each case from a database storing processing results of respective performed jobs to generate, for each case, a process instance including job names and time data of the performed jobs arranged in a time series. Then, process instances satisfying a designated condition are classified among the process instances to calculate, for each classification, an average value and standard deviation of transition periods that are differences between a start time and an end time of a job section to be processed in the pertinent process instances. By identifying a process instance, which has correlation with the route before the job section and for which the reduction of the transition period is expected and/or a process instance, which has correlation with the entire route and for which the reduction of the transition period is expected, an improvement candidate is automatically shown to the user.

Abstract: A process-structure estimating method, includes counting a number of the events executed in parallel and are added-numbers; generating virtual route data including a start point and an end point for the events for which no added-number attribute is set, a branch point coupled to the start point and branched into branch routes for corresponding pairs of the added-number attributes and the attribute values of the added-number attributes, and a merge point at which the branch routes are merged together, the merge point being coupled to the end point; determining, the branch route on which the added-number-attribute-set event is to be placed on the virtual route data based on the added-number attributes, values thereof, processing times associated therewith, and updating the virtual route data based on the branch route.

Abstract: A series of transaction records is extracted for each case from a database storing processing results of respective performed jobs to generate, for each case, a process instance including job names and time data of the performed jobs arranged in a time series. Then, process instances satisfying a designated condition are classified among the process instances to calculate, for each classification, an average value and standard deviation of transition periods that are differences between a start time and an end time of a job section to be processed in the pertinent process instances. By identifying a process instance, which has correlation with the route before the job section and for which the reduction of the transition period is expected and/or a process instance, which has correlation with the entire route and for which the reduction of the transition period is expected, an improvement candidate is automatically shown to the user.

Abstract: In order to identify a parallel-executed portion of processes included in a business flow, following stages are executed: obtaining transaction records including a processing time, a process type and an identifier from a database; grouping the transaction records based on the identifier and sorting the transaction records within a group based on the processing time; in case where the processing time included in the transaction record represents only one time, calculating a processing period of each transaction record based on a sequence of the processing times in the transaction records in the group; identifying a transaction record relating to a process executed in parallel based on the distribution of the calculated processing periods for each process type; and identifying, for each group, a business flow of the group based on the processing result of the identifying the transaction record and the transaction records in the group.

Abstract: An apparatus includes an extracting unit which specifies a most frequent occurrence position section regarding an event category of an event in a first flow data and associates a most frequent occurrence group including the most frequent occurrence position section and the event category, and a display unit which displays a node included in second flow data and an occurrence position section of the node and displays the event category and the most frequent occurrence group.