ARTIFICIAL INTELLIGENCE DRIVEN CONFIGURATION MANAGEMENT
Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments ofa software resource. Based on varying values in the feature set, the process clusters each of the plu...
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| creator | GANESH AMIT KRISHNAN SHRIRAM GARVEY DUSTIN FRAZIER TIMOTHY MARK YANG LONG RAVURI PRASAD GOPALAKRISHNAN SUMATHI SHAFT URI SALUNKE SAMPANNA SHAHAJI |
| description | Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments ofa software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
本文描述了用于人工智能驱动的配置管理的技术。在一些实施例中，机器学习过程确定用于软件资源的多个部署的特征集。基于特征集中的变化值，该过程将该多个部署中的每个部署聚类到多个聚类中的聚类中。该多个聚类中的每个聚类包括一个或多个节点，并且该一个或多个节点中的每个节点对应于在软件资源的该多个部署中的至少一个部署中被检测到的特征集的值的至少一个子集。该过程为该多个聚类中的每个聚类确定代表性节点。可以基于至少一个聚类 |
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本文描述了用于人工智能驱动的配置管理的技术。在一些实施例中，机器学习过程确定用于软件资源的多个部署的特征集。基于特征集中的变化值，该过程将该多个部署中的每个部署聚类到多个聚类中的聚类中。该多个聚类中的每个聚类包括一个或多个节点，并且该一个或多个节点中的每个节点对应于在软件资源的该多个部署中的至少一个部署中被检测到的特征集的值的至少一个子集。该过程为该多个聚类中的每个聚类确定代表性节点。可以基于至少一个聚类</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200619&DB=EPODOC&CC=CN&NR=111316231A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200619&DB=EPODOC&CC=CN&NR=111316231A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>GANESH AMIT</creatorcontrib><creatorcontrib>KRISHNAN SHRIRAM</creatorcontrib><creatorcontrib>GARVEY DUSTIN</creatorcontrib><creatorcontrib>FRAZIER TIMOTHY MARK</creatorcontrib><creatorcontrib>YANG LONG</creatorcontrib><creatorcontrib>RAVURI PRASAD</creatorcontrib><creatorcontrib>GOPALAKRISHNAN SUMATHI</creatorcontrib><creatorcontrib>SHAFT URI</creatorcontrib><creatorcontrib>SALUNKE SAMPANNA SHAHAJI</creatorcontrib><title>ARTIFICIAL INTELLIGENCE DRIVEN CONFIGURATION MANAGEMENT</title><description>Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments ofa software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
本文描述了用于人工智能驱动的配置管理的技术。在一些实施例中，机器学习过程确定用于软件资源的多个部署的特征集。基于特征集中的变化值，该过程将该多个部署中的每个部署聚类到多个聚类中的聚类中。该多个聚类中的每个聚类包括一个或多个节点，并且该一个或多个节点中的每个节点对应于在软件资源的该多个部署中的至少一个部署中被检测到的特征集的值的至少一个子集。该过程为该多个聚类中的每个聚类确定代表性节点。可以基于至少一个聚类</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDB3DArxdPN09nT0UfD0C3H18fF0d_VzdlVwCfIMc_VTcPb3c_N0Dw1yDPH091PwdfRzdHf1dfUL4WFgTUvMKU7lhdLcDIpuriHOHrqpBfnxqcUFicmpeakl8c5-hoaGxoZmRsaGjsbEqAEAUIYoTA</recordid><startdate>20200619</startdate><enddate>20200619</enddate><creator>GANESH AMIT</creator><creator>KRISHNAN SHRIRAM</creator><creator>GARVEY DUSTIN</creator><creator>FRAZIER TIMOTHY MARK</creator><creator>YANG LONG</creator><creator>RAVURI PRASAD</creator><creator>GOPALAKRISHNAN SUMATHI</creator><creator>SHAFT URI</creator><creator>SALUNKE SAMPANNA SHAHAJI</creator><scope>EVB</scope></search><sort><creationdate>20200619</creationdate><title>ARTIFICIAL INTELLIGENCE DRIVEN CONFIGURATION MANAGEMENT</title><author>GANESH AMIT ; KRISHNAN SHRIRAM ; GARVEY DUSTIN ; FRAZIER TIMOTHY MARK ; YANG LONG ; RAVURI PRASAD ; GOPALAKRISHNAN SUMATHI ; SHAFT URI ; SALUNKE SAMPANNA SHAHAJI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN111316231A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>GANESH AMIT</creatorcontrib><creatorcontrib>KRISHNAN SHRIRAM</creatorcontrib><creatorcontrib>GARVEY DUSTIN</creatorcontrib><creatorcontrib>FRAZIER TIMOTHY MARK</creatorcontrib><creatorcontrib>YANG LONG</creatorcontrib><creatorcontrib>RAVURI PRASAD</creatorcontrib><creatorcontrib>GOPALAKRISHNAN SUMATHI</creatorcontrib><creatorcontrib>SHAFT URI</creatorcontrib><creatorcontrib>SALUNKE SAMPANNA SHAHAJI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>GANESH AMIT</au><au>KRISHNAN SHRIRAM</au><au>GARVEY DUSTIN</au><au>FRAZIER TIMOTHY MARK</au><au>YANG LONG</au><au>RAVURI PRASAD</au><au>GOPALAKRISHNAN SUMATHI</au><au>SHAFT URI</au><au>SALUNKE SAMPANNA SHAHAJI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>ARTIFICIAL INTELLIGENCE DRIVEN CONFIGURATION MANAGEMENT</title><date>2020-06-19</date><risdate>2020</risdate><abstract>Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments ofa software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
本文描述了用于人工智能驱动的配置管理的技术。在一些实施例中，机器学习过程确定用于软件资源的多个部署的特征集。基于特征集中的变化值，该过程将该多个部署中的每个部署聚类到多个聚类中的聚类中。该多个聚类中的每个聚类包括一个或多个节点，并且该一个或多个节点中的每个节点对应于在软件资源的该多个部署中的至少一个部署中被检测到的特征集的值的至少一个子集。该过程为该多个聚类中的每个聚类确定代表性节点。可以基于至少一个聚类</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | ARTIFICIAL INTELLIGENCE DRIVEN CONFIGURATION MANAGEMENT |
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