公开(公告)号：US20190266619A1

公开(公告)日：2019-08-29

申请号：US16122924

申请日：2018-09-06

Applicant: Hitachi, Ltd.

Inventor： Hiroyuki NAMBA ,  Masaharu UKEDA

IPC: G06Q30/02 ,  G06F17/30

Abstract: A change in a behavior pattern contributing to improving a Key Performance Indicator (KPI) is discovered from among changes in the behavior pattern arising in common among customers having a specific characteristic. A behavior pattern search system classifies transfiguration candidates that are customers satisfying transfiguration result information. The transfiguration result information indicates a characteristic purchase behavior predicted to occur by a change in a behavior pattern of each of the customers.

公开(公告)号：US20240103930A1

公开(公告)日：2024-03-28

申请号：US17950194

申请日：2022-09-22

Applicant: Hitachi, Ltd.

Inventor： Toshio NISHIDA ,  Masaharu UKEDA

IPC: G06F9/50

CPC classification number: G06F9/5072 ,  G06F2209/501 ,  G06F2209/502

Abstract: An environment construction system holds resource price information indicating a price of available resources in a second environment and communication distance information indicating a distance of a communication path between the second environment and a first environment when the available resources are used for the second environment, acquires system configuration information of an information system and environment arrangement information indicating a physical location where the first environment is disposed, analyzes the system configuration information to generate an analysis result indicating a configuration of the system, determines an environment arrangement policy indicating a requirement required for the second environment based on the analysis result, selects a resource to be used for the second environment based on the resource price information, the communication distance information, and the environment arrangement policy, converts the system configuration information to use the selected resource, and constructs the second environment using the selected resource.

公开(公告)号：US20210365813A1

公开(公告)日：2021-11-25

申请号：US17209341

申请日：2021-03-23

Applicant: Hitachi, Ltd.

Inventor： Kaori NAKANO ,  Masaharu UKEDA ,  Soichi TAKASHIGE ,  Yuxin LIANG

IPC: G06N5/04 ,  G06N20/00

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.

公开(公告)号：US20210141538A1

公开(公告)日：2021-05-13

申请号：US17012423

申请日：2020-09-04

Applicant: Hitachi, Ltd.

Inventor： Hideyuki SAKAI ,  Masaharu UKEDA ,  Tomoya OHTA ,  Soichi TAKASHIGE ,  Shinichi HAYASHI

IPC: G06F3/06

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.