Abstract: There are provided a device that collects operation data from equipment, and an information processing apparatus that detects an anomaly or an omen of an anomaly of the equipment on the basis of anomaly detection models constructed from the operation data, the information processing apparatus including means for collecting the operation data, means for learning anomaly detection models from the operation data, and means for calculating an anomaly score of respective operation data from the operation data and the anomaly detection models, the means for learning an anomaly detection model in which a dispersion of elements is small among the anomaly detection models.

Abstract: A system calculates an inter-distribution distance between target data and base data for each of one or more second periods in an entire period between an end time of a first period and a relearning timing of a learning model. For each second period, the target data is data for the second period in the entire data that is data for the entire period. The base data is data for the first period and is data related to creation of the learning model. The system specifies, as a variation point, a time in accordance with the second period during which the calculated inter-distribution distance is greater than or equal to a threshold. The system determines and outputs one or more training data candidates from a part or all of the entire data based on the one or more variation points for relearning of the learning model.

Abstract: The time-series pattern explanatory information generating apparatus includes a prediction model learning unit configured to learn time-series data acquired from a monitored system in a neural network to construct a prediction model. A candidate sequence pattern generating unit is configured to use the prediction model to extract candidate sequence patterns included in time-series data and indicating a characteristic change.

Abstract: A data generation device generates a data set and includes: a perturbation generation unit that generates a perturbation set for deforming each element based on at least one of an input of each element of a training data set and information on the training data set; a pseudo data synthesis unit that generates a new pseudo data set different from the training data set from the training data set and the perturbation set; an evaluation unit that calculates a distributional distance of the training data set and the pseudo data set or an estimated amount of the distributional distance and a magnitude of perturbation of the pseudo data with respect to the training data obtained from the perturbation set; and a parameter update unit that updates a parameter used by the perturbation generation unit to generate the perturbation set so that the distributional distance of the training data set and the pseudo data set is close to each other and the magnitude or expected value of the perturbation becomes a predetermined t

Abstract: There are provided a device that collects operation data from equipment, and an information processing apparatus that detects an anomaly or an omen of an anomaly of the equipment on the basis of anomaly detection models constructed from the operation data, the information processing apparatus including means for collecting the operation data, means for learning anomaly detection models from the operation data, and means for calculating an anomaly score of respective operation data from the operation data and the anomaly detection models, the means for learning an anomaly detection model in which a dispersion of elements is small among the anomaly detection models.

Abstract: Provided is a technique for extracting a factor (event pattern) that has an influence on an objective index (objective variable). A data analysis device according to the present disclosure performs: a process of generating, with respect to explanatory variable data included in data to be analyzed, a time-series pattern in a predetermined range; a process of calculating a correlation value between the time-series pattern and at least one item of objective variable data included in the data to be analyzed; and a process of outputting, together with the correlation value, the time-series pattern corresponding to the correlation value as an analysis result.

Abstract: An on-site system includes a disclosure-condition-table storage unit and a disclosure-condition-table creation unit. The disclosure-condition-table storage unit stores a disclosure condition table for management of a relationship between a purpose of a service and a data characteristic of on-site data, as a disclosure condition for disclosing the on-site data to a service device that provides the service. The disclosure-condition-table creation unit creates the disclosure condition table in which a purpose of a service included in descriptive information of the service is associated with a data characteristic included in descriptive information of the on-site data, and stores the disclosure condition table in the disclosure-condition-table storage unit.

Abstract: A time series data monitoring system includes: a series pattern candidate generating unit that generates series pattern candidates included in time series data obtained from a monitored system using the time series data and a prediction model of the time series data; and a series pattern generating unit that classifies the series pattern candidates generated by the series pattern candidate generating unit and outputs, as a series pattern of the time series data, a candidate satisfying a predetermined condition among the classified series pattern candidates.

Abstract: An on-site system includes a disclosure-condition-table storage unit and a disclosure-condition-table creation unit. The disclosure-condition-table storage unit stores a disclosure condition table for management of a relationship between a purpose of a service and a data characteristic of on-site data, as a disclosure condition for disclosing the on-site data to a service device that provides the service. The disclosure-condition-table creation unit creates the disclosure condition table in which a purpose of a service included in descriptive information of the service is associated with a data characteristic included in descriptive information of the on-site data, and stores the disclosure condition table in the disclosure-condition-table storage unit.

Abstract: An objective is to set an anomaly detection threshold easily and accurately. An anomaly detection system 1 includes an arithmetic device 1H101 that executes processing of learning a predictive model that predicts a behavior of a monitoring target device based on operational data on the device, processing of adjusting an anomaly score such that the anomaly score for operational data under normal operation falls within a predetermined range, the anomaly score being based on a deviation of the operational data acquired from the monitoring target device from a prediction result obtained by the predictive model, processing of detecting an anomaly or a sign of an anomaly based on the adjusted anomaly score, and processing of displaying information on at least one of the anomaly score and a result of the detection on an output device.

Abstract: Provided is a technique for extracting a factor (event pattern) that has an influence on an objective index (objective variable). A data analysis device according to the present disclosure performs: a process of generating, with respect to explanatory variable data included in data to be analyzed, a time-series pattern in a predetermined range; a process of calculating a correlation value between the time-series pattern and at least one item of objective variable data included in the data to be analyzed; and a process of outputting, together with the correlation value, the time-series pattern corresponding to the correlation value as an analysis result.

Abstract: An abnormality detection system is configured to (a) convert, based on a prescribed rule, a time-sequential event included in a log output by a monitoring target system into a symbolized event; (b) learn, based on a normal-time log symbolized in (a), a symbolized event sequence, which appears in a same pattern, as a frequently-appearing pattern; and (c) detect an occurrence or a nonoccurrence of an abnormality, based on whether not the frequently-appearing pattern is occurring in a monitoring-time log symbolized in (a).

Abstract: A calculation device is provided that executes calculations within real-time restrictions. The calculation device implements a step of predicting a processing time of a calculation related to the amount and property of input data based on a prediction model; a step of adjusting the processing time by decreasing the amount of data used for the calculation or decreasing the number of iterative calculations when the processing time exceeds a time slice allocated to the calculation; a step of executes the calculation using the adjusted processing time; a step of updating, as required, the prediction model used for predicting the processing time according to the result of the calculation which is executed in a period where the calculation is not performed while implementing a change of the amount of data or the number of iterative calculations or change to an approximation.

Abstract: Precise prediction of the state of congestion in an automatic analysis system and adjustment of timings and the order of take-in and take-out of specimens in each of devices prevent increase in TATs of urgent specimens. A device management server for instructing each device to take in or take out specimens in each device determines the current position of each specimen, estimates the staying time of each specimen in one of regions by simulating the operation of each device assuming an initial state in which each specimen is at the current position, causes the simulation unit to perform a simulation, and corrects the timings or order of take-in and take-out of one or more other specimens so that each of the staying times in one of the regions do not exceed a corresponding allowable staying time in the region.

Abstract: There is provided a sample test system which can execute a prior process for a specific sample in consideration of priorities and process progresses of a plurality of samples processed in the sample test system. The sample test system includes a sample-test-device management server 101, when a specific-sample prior process request containing a specific sample ID and a prior-process-request level identification code is accepted from a sample access system 108, which selects a specific-sample prior-process-policy determination table based on the prior-process-request level identification code attached to the specific-sample prior process request, which determines a specific-sample prior process policy based on the specific-sample prior-process-policy determination table and a process state of the sample, which specifies a sample ID regarding the specific sample ID, and which transmits the specific-sample prior process policy to a sample-test-device group 119.

Abstract: A calculation device is provided that executes calculations within real-time restrictions. The calculation device implements a step of predicting a processing time of a calculation related to the amount and property of input data based on a prediction model; a step of adjusting the processing time by decreasing the amount of data used for the calculation or decreasing the number of iterative calculations when the processing time exceeds a time slice allocated to the calculation; a step of executes the calculation using the adjusted processing time; a step of updating, as required, the prediction model used for predicting the processing time according to the result of the calculation which is executed in a period where the calculation is not performed while implementing a change of the amount of data or the number of iterative calculations or change to an approximation.

Abstract: Precise prediction of the state of congestion in an automatic analysis system and adjustment of timings and the order of take-in and take-out of specimens in each of devices prevent increase in TATs of urgent specimens. A device management server for instructing each device to take in or take out specimens in each device determines the current position of each specimen, estimates the staying time of each specimen in one of regions by simulating the operation of each device assuming an initial state in which each specimen is at the current position, causes the simulation unit to perform a simulation, and corrects the timings or order of take-in and take-out of one or more other specimens so that each of the staying times in one of the regions do not exceed a corresponding allowable staying time in the region.

Abstract: There is provided a sample test system which can execute a prior process for a specific sample in consideration of priorities and process progresses of a plurality of samples processed in the sample test system. The sample test system includes a sample-test-device management server 101, when a specific-sample prior process request containing a specific sample ID and a prior-process-request level identification code is accepted from a sample access system 108, which selects a specific-sample prior-process-policy determination table based on the prior-process-request level identification code attached to the specific-sample prior process request, which determines a specific-sample prior process policy based on the specific-sample prior-process-policy determination table and a process state of the sample, which specifies a sample ID regarding the specific sample ID, and which transmits the specific-sample prior process policy to a sample-test-device group 119.