公开(公告)号：US20150363250A1

公开(公告)日：2015-12-17

申请号：US14764272

申请日：2014-02-05

Applicant: NEC CORPORATION

Inventor： Kentarou YABUKI

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G05B23/0254 ,  G06F11/0721 ,  G06F11/3409 ,  G06F11/3452

Abstract: In state detection of a system using a correlation destruction pattern, the versatility of the correlation destruction pattern is improved. A system analysis device (100) includes a correlation destruction pattern storage unit (113), an aggregated destruction pattern generation unit (104), and a similarity calculation unit (105). The correlation destruction pattern storage unit (113) stores a plurality of correlation destruction patterns (123) each of which is a set of correlations in which correlation destruction has been detected among correlations of pairs of metrics in a system. The aggregated destruction pattern generation unit (104) generates an aggregated destruction pattern (124) which is obtained by aggregating correlation destruction patterns (123) of the same type among the plurality of correlation destruction patterns (123). The similarity calculation unit (105) calculates and outputs a similarity between the aggregated destruction pattern (124) and a newly-detected correlation destruction pattern (123).

Abstract translation: 在使用相关破坏模式的系统的状态检测中，相关破坏模式的通用性得到改善。 系统分析装置（100）包括相关破坏模式存储单元（113），聚合破坏模式生成单元（104）和相似度计算单元（105）。 相关破坏模式存储单元（113）存储多个相关破坏模式（123），每个相关破坏模式（123）是一组相关性，其中已经在系统中的相关度量标准相关中检测到相关破坏。 聚合破坏模式生成部（104）生成通过聚合多个相关破坏模式（123）中的相同类型的相关破坏模式（123）而获得的聚合销毁模式（124）。 相似度计算单元（105）计算并输出聚合销毁模式（124）和新检测的相关破坏模式（123）之间的相似度。

公开(公告)号：US20180052726A1

公开(公告)日：2018-02-22

申请号：US15557865

申请日：2016-03-15

Applicant: NEC CORPORATION

Inventor： Shinichiro YOSHIDA ,  Kentarou YABUKI ,  Kiyoshi KATO ,  Hideo HASEGAWA ,  Hiroyuki MIYAZAKI

IPC: G06F11/07 ,  G05B23/02 ,  G06F17/30

Abstract: Accuracy of risks defined for abnormalities that might occur in a system is improved. The risk determination device 100 includes a classification unit 114 and a determination unit 115. The classification unit 114 classifies abnormal patterns 133, each representing a relationship among metrics at a time of abnormality detection in a system, into groups 134 based on a similarity between the abnormal patterns. The determination unit 115 determines, based on the number of abnormal patterns 133 classified into each of the groups 134, likelihood of an abnormality of the corresponding group 134.

公开(公告)号：US20180032640A1

公开(公告)日：2018-02-01

申请号：US15556939

申请日：2016-03-09

Applicant: NEC CORPORATION

Inventor： Kentarou YABUKI

IPC: G06F17/50

Abstract: Monitoring of a state of a system can be accurately performed even when a timing at which a relation changes is different for each set of the metrics. A monitoring apparatus 100 includes a model storage unit 122 and a determination unit 115. The model storage unit 122 stores, for each of a plurality of metric sets in a system, a model representing a relation among metrics included in the corresponding metric set. The determination unit 115 determines and outputs whether the system is in one state, by comparing a combination of models to which the plurality of metric sets conform respectively when the system is in the one state and a combination of models to which the plurality of metric sets conform respectively when the system is in a state to be determined.

公开(公告)号：US20170083605A1

公开(公告)日：2017-03-23

申请号：US15126316

申请日：2015-03-03

Applicant: NEC Corporation

Inventor： Kentarou YABUKI

IPC: G06F17/30 ,  G06K9/62

CPC classification number: G06F16/285 ,  G06F16/23 ,  G06K9/4633 ,  G06K9/622 ,  G06K9/6247 ,  G06K9/6285

Abstract: Fast classification of data can be performed according to characteristics. In a clustering device (100), a data storage unit (300) stores a plurality of data sets. A cluster generation unit (400) generates an approximate line that approximates as many data sets as possible within a predetermined margin of error among the plurality of data sets in a space in which the plurality of data sets are arranged in accordance with data values. The cluster generation unit (400) generates a cluster by classifying the plurality of data sets based on the generated approximate line and outputs the generated cluster.