Abstract: In a data management system of a machine learning model, flag management information (a flag importance management table) manages and defines respective flags corresponding to, of a plurality of processes included in the life cycle, one or more predetermined processes. An operation unit assigns flags defined in the flag management information to input data and output data of the model in accordance with involvement in the predetermined processes when the model is operated. A data management unit determines, with respect to each of the input data and the output data, the necessity of storage of data on the basis of a flag assigned to the data by the operation unit.

Abstract: When an event such as maintenance occurs, a model in which a tendency of input data changes due to an influence of the maintenance and retraining is required is specified. Model identifiers that specify the machine learning models and are unique to the machine learning models and sensor identifiers that specify the sensors that output the sensor data serving as the input data of the machine learning models and are unique to the sensors are managed to be in association with each other. A degree of influence indicating a change in tendency of the sensor data before and after the maintenance operation is performed is obtained for each maintenance event identifier that specifies a maintenance operation performed on a device and is unique to the maintenance operation, and the degree of influence is managed in association with each of the sensor identifiers.

Abstract: Provided is an application development support system and method that support studies on applying serverless while optimizing application resource usage amount and maintaining API response performance in developing an application with microservices. In the application development support system, a container runtime platform applies serverless to a certain container to form an event-driven container. The application development support system executes an API processing time prediction process by calculating a predicted value of API processing time in the event-driven container and a predicted value of API processing time in all of the containers based on the predicted value and a sequence of API calls among the plurality of containers; calculating a usage amount of resources used by the plurality of containers per unit period; and outputting a calculated result of each of the API processing time prediction processing and the resource usage amount calculation processing.

Abstract: Provided is a data management method capable of deleting intermediate data at an appropriate timing. The data management method in a data analysis system that performs analysis by combining a plurality of input data based on an analysis execution request from a computer includes: a first step, in which a request analysis unit analyzes the analysis execution request from the computer to identify a task, identifies intermediate data generated after execution of each identified task, and generates constraint information that determines whether to delete the identified intermediate data; a second step, in which a task management unit determines whether to delete the intermediate data based on the constraint information for each identified task; and a third step, in which a task execution unit executes the identified task and deletes the intermediate data of the task based on a determination result of the second step.

Abstract: Status information including remaining battery power is collected from a management target device. Subsequent operations of the device are identified from an operation schedule management collection. Battery consumptions corresponding to subsequent operations of the device are estimated from KPI management collection that includes information representing relationships between battery consumptions and operations with respect to each of a plurality of devices. The battery life of the device is predicted based on the estimated battery consumptions and the remaining battery power represented by the collected status information. A circuit schedule of a maintenance engineer is determined based on the predicted battery life, the schedule including necessity of dispatch of the maintenance engineer to the device.

Abstract: The present invention ensures improving responsiveness in a P2P type distributed database system that includes a plurality of node computers capable of storing management target data in storage devices. The storage devices include a first type storage device having a high input/output performance and a second type storage device having a low input/output performance. There is provided the storage devices that store KeySpace management tables including response count information indicative of a count of the node computers from which the identical management target data are to be read. The identical management target data are required to respond to a read request of the management target data. The node computers by a count indicated by the response count information and capable of storing the management target data in the first type storage devices among the plurality of node computers are determined as storage destinations when a CPU receives a write request of new management target data.

Abstract: The present invention proposes an information processing system and a volume allocation method making it feasible to reduce overall system operation cost. A replication group to carry out replication is comprised of one or multiple nodes. A storage node classifies respective storage devices mounted thereon into plural storage hierarchies according to the drive type of each of the storage devices for management of the storage devices. A controller node allocates, to each node of the nodes constituting the replication group, a volume for which a storage device of a storage hierarchy suitable for a type of middleware that the node uses and a node type of the node acting in the replication group provides a storage area.

Abstract: A management computer for managing a system that makes an inference using a training model has a processor for performing a process in cooperation with a memory, and the processor executes: a generation process for generating an accuracy improvement prediction model for predicting the accuracy of a retrained model when retraining is executed using retraining data including new collected data collected from the system after the start of the operation of the system based on a correlation between the Feature of training data used for training of the training model and the accuracy of the training model; a prediction process for predicting the accuracy of the retrained model from the accuracy improvement prediction model and the Feature of the retraining data; and a determination process for determining whether or not the execution of the retraining is necessary based on the predicted accuracy of the retrained model.

Abstract: To efficiently and appropriately improve an inference model when the inference model outputs an inappropriate inference result that affects work. A model improvement system performs inference related to the work using an inference model which is a machine learning model that outputs an inference result by inputting an input matrix, acquires an actual result which is information acquired by actually performing work to be inferred by the inference model, determines whether or not the inference result is an inappropriate content that affects the work by comparing the inference result with the actual result, divides a part of the input matrix that outputs the inference result that affects the work from the input matrix, and generates an inference model that outputs an inference result that does not affect the work when the part divided from the input matrix is input as an input matrix.

Abstract: Provided is an application development support system and method that support studies on applying serverless while optimizing application resource usage amount and maintaining API response performance in developing an application with microservices. In the application development support system, a container runtime platform applies serverless to a certain container to form an event-driven container. The application development support system executes an API processing time prediction process by calculating a predicted value of API processing time in the event-driven container and a predicted value of API processing time in all of the containers based on the predicted value and a sequence of API calls among the plurality of containers; calculating a usage amount of resources used by the plurality of containers per unit period; and outputting a calculated result of each of the API processing time prediction processing and the resource usage amount calculation processing.

Abstract: Volume deployment is optimized while adopting layering corresponding to IO density is adopted. A storage management apparatus calculates remaining IO density based on an amount of remaining IOPS and an amount of a remaining capacity in IOPS and a capacity that are providable by a node, the remaining IOPS and the remaining capacity being not allocated to any volume, controls the layers of the node based on the remaining IO density, selects a node on which a volume is to be deployed based on the difference between the IO density of a deployment target volume and the remaining IO density of the node, and determines a node having the smallest difference between the IO density of the deployment target volume and the remaining IO density of the node in the selection of the node as a deployment destination of the volume.

Abstract: Status information including remaining battery power is collected from a management target device. Subsequent operations of the device are identified from an operation schedule management collection. Battery consumptions corresponding to subsequent operations of the device are estimated from KPI management collection that includes information representing relationships between battery consumptions and operations with respect to each of a plurality of devices. The battery life of the device is predicted based on the estimated battery consumptions and the remaining battery power represented by the collected status information. A circuit schedule of a maintenance engineer is determined based on the predicted battery life, the schedule including necessity of dispatch of the maintenance engineer to the device.

Abstract: The present invention ensures improving responsiveness in a P2P type distributed database system that includes a plurality of node computers capable of storing management target data in storage devices. The storage devices include a first type storage device having a high input/output performance and a second type storage device having a low input/output performance. There is provided the storage devices that store KeySpace management tables including response count information indicative of a count of the node computers from which the identical management target data are to be read. The identical management target data are required to respond to a read request of the management target data. The node computers by a count indicated by the response count information and capable of storing the management target data in the first type storage devices among the plurality of node computers are determined as storage destinations when a CPU receives a write request of new management target data.

Abstract: Provided is a data management method capable of deleting intermediate data at an appropriate timing. The data management method in a data analysis system that performs analysis by combining a plurality of input data based on an analysis execution request from a computer includes: a first step, in which a request analysis unit analyzes the analysis execution request from the computer to identify a task, identifies intermediate data generated after execution of each identified task, and generates constraint information that determines whether to delete the identified intermediate data; a second step, in which a task management unit determines whether to delete the intermediate data based on the constraint information for each identified task; and a third step, in which a task execution unit executes the identified task and deletes the intermediate data of the task based on a determination result of the second step.

Abstract: The present invention proposes an information processing system and a volume allocation method making it feasible to reduce overall system operation cost. A replication group to carry out replication is comprised of one or multiple nodes. A storage node classifies respective storage devices mounted thereon into plural storage hierarchies according to the drive type of each of the storage devices for management of the storage devices. A controller node allocates, to each node of the nodes constituting the replication group, a volume for which a storage device of a storage hierarchy suitable for a type of middleware that the node uses and a node type of the node acting in the replication group provides a storage area.

Abstract: To reduce cumbersomeness in manually setting backup requirements per middleware. Not only one or more deployment requirements but also one or more first backup requirements are associated with a middleware deployment indication. In response to the indication, middleware deployment and backup setting are performed. The middleware deployment includes associating volumes (VOLs) the number of which complies with the one or more deployment requirements, with virtual machines (VMs) the number of which complies with the one or more deployment requirements. The backup setting includes associating two or more backup requirements based on the one or more first backup requirements and one or more predetermined second backup requirements in response to a middleware type of middleware to be deployed, with the middleware deployment.

Abstract: A computer system comprising computers, wherein the computers include a first computer and second computers for providing resources to a business system. The business system includes a first VM capable of changing its performance by executing scale-out, and a second VMs capable of changing their performance by executing scale-up. The resource optimizing module included in the first computer is configured to: execute first processing for applying resource changing methods that are light in processing load to the active second VM and the standby second VM in a case of detecting an incident of an increase in load on the active second VM; and execute second processing for applying resource changing methods that are heavy in processing load to the active second VM and the standby second VM in a case of increasing the load on the active second VM.

Abstract: Standby computers are dynamically divided into groups according to the difference between the software thereof and the software required of a business system. When a computer is made available, the standby computers divided into groups by software structure are searched and an appropriate one is extracted to quickly complete the construction of the software environment. An active computer, if to be transferred to standby mode, is associated with the group having the same software structure as the business system with which the active computer has thus far operated. The active/standby states of the computers are monitored, and the standby computers are changed thereby to widen the possible range of application to other business systems, removing copy operation of disk images from the active computer to the standby computers.

Abstract: Standby computers are dynamically divided into groups according to the difference between the software thereof and the software required of a business system. When a computer is made available, the standby computers divided into groups by software structure are searched and an appropriate one is extracted to quickly complete the construction of the software environment. An active computer, if to be transferred to standby mode, is associated with the group having the same software structure as the business system with which the active computer has thus far operated. The active/standby states of the computers are monitored, and the standby computers are changed thereby to widen the possible range of application to other business systems, removing copy operation of disk images from the active computer to the standby computers.

Abstract: Standby computers are dynamically divided into groups according to the difference between the software thereof and the software required of a business system. When a computer is made available, the standby computers divided into groups by software structure are searched and an appropriate one is extracted to quickly complete the construction of the software environment. An active computer, if to be transferred to standby mode, is associated with the group having the same software structure as the business system with which the active computer has thus far operated. The active/standby states of the computers are monitored, and the standby computers are changed thereby to widen the possible range of application to other business systems, removing copy operation of disk images from the active computer to the standby computers.