Storage Anomaly Detection
The technology described in this document is, among other things, capable of efficiently monitoring storage device signal data for anomalies. In an example method, signal data for a plurality of non-transitory storage devices is collected. The method determines a hyper feature representation from th...
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| creator | Shivaji Shivkumar Sudhakaran Ryan Barajas Zamora Joel Lee Yong Bum |
| description | The technology described in this document is, among other things, capable of efficiently monitoring storage device signal data for anomalies. In an example method, signal data for a plurality of non-transitory storage devices is collected. The method determines a hyper feature representation from the collected signal data and computes, using the hyper feature representation, scores for statistics associated with the non-transitory storage devices. The method further determines a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices; generates, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively; and identifies one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores. |
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In an example method, signal data for a plurality of non-transitory storage devices is collected. The method determines a hyper feature representation from the collected signal data and computes, using the hyper feature representation, scores for statistics associated with the non-transitory storage devices. The method further determines a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices; generates, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively; and identifies one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.</description><language>eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2018</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=20180201&DB=EPODOC&CC=US&NR=2018032385A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20180201&DB=EPODOC&CC=US&NR=2018032385A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Shivaji Shivkumar</creatorcontrib><creatorcontrib>Sudhakaran Ryan</creatorcontrib><creatorcontrib>Barajas Zamora Joel</creatorcontrib><creatorcontrib>Lee Yong Bum</creatorcontrib><title>Storage Anomaly Detection</title><description>The technology described in this document is, among other things, capable of efficiently monitoring storage device signal data for anomalies. In an example method, signal data for a plurality of non-transitory storage devices is collected. The method determines a hyper feature representation from the collected signal data and computes, using the hyper feature representation, scores for statistics associated with the non-transitory storage devices. The method further determines a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices; generates, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively; and identifies one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.</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>2018</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZJAMLskvSkxPVXDMy89NzKlUcEktSU0uyczP42FgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFicmpeakl8aHBRgaGFgbGRsYWpo6GxsSpAgCeECN5</recordid><startdate>20180201</startdate><enddate>20180201</enddate><creator>Shivaji Shivkumar</creator><creator>Sudhakaran Ryan</creator><creator>Barajas Zamora Joel</creator><creator>Lee Yong Bum</creator><scope>EVB</scope></search><sort><creationdate>20180201</creationdate><title>Storage Anomaly Detection</title><author>Shivaji Shivkumar ; Sudhakaran Ryan ; Barajas Zamora Joel ; Lee Yong Bum</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2018032385A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2018</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Shivaji Shivkumar</creatorcontrib><creatorcontrib>Sudhakaran Ryan</creatorcontrib><creatorcontrib>Barajas Zamora Joel</creatorcontrib><creatorcontrib>Lee Yong Bum</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shivaji Shivkumar</au><au>Sudhakaran Ryan</au><au>Barajas Zamora Joel</au><au>Lee Yong Bum</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Storage Anomaly Detection</title><date>2018-02-01</date><risdate>2018</risdate><abstract>The technology described in this document is, among other things, capable of efficiently monitoring storage device signal data for anomalies. In an example method, signal data for a plurality of non-transitory storage devices is collected. The method determines a hyper feature representation from the collected signal data and computes, using the hyper feature representation, scores for statistics associated with the non-transitory storage devices. The method further determines a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices; generates, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively; and identifies one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Storage Anomaly Detection |
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