ANOMALY AND CAUSATION DETECTION IN COMPUTING ENVIRONMENTS
Anomaly detection in computing environments is disclosed herein. An example method includes receiving an unstructured input stream of data instances from the computing environment, the unstructured input stream being time stamped; categorizing the data instances of the unstructured input stream of d...
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| creator | DODSON, Stephen ROBERTS, David Mark VEASEY, Thomas |
| description | Anomaly detection in computing environments is disclosed herein. An example method includes receiving an unstructured input stream of data instances from the computing environment, the unstructured input stream being time stamped; categorizing the data instances of the unstructured input stream of data instances, the data instances comprising at least one principle value and a set of categorical attributes determined through machine learning; generating anomaly scores for each of the data instances collected over a period of time; and detecting a change in the categorical attribute that is indicative of an anomaly. |
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An example method includes receiving an unstructured input stream of data instances from the computing environment, the unstructured input stream being time stamped; categorizing the data instances of the unstructured input stream of data instances, the data instances comprising at least one principle value and a set of categorical attributes determined through machine learning; generating anomaly scores for each of the data instances collected over a period of time; and detecting a change in the categorical attribute that is indicative of an anomaly.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRIC DIGITAL DATA PROCESSING ; ELECTRICITY ; PHYSICS ; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</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=20201014&DB=EPODOC&CC=EP&NR=3616096A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25544,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201014&DB=EPODOC&CC=EP&NR=3616096A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>DODSON, Stephen</creatorcontrib><creatorcontrib>ROBERTS, David Mark</creatorcontrib><creatorcontrib>VEASEY, Thomas</creatorcontrib><title>ANOMALY AND CAUSATION DETECTION IN COMPUTING ENVIRONMENTS</title><description>Anomaly detection in computing environments is disclosed herein. An example method includes receiving an unstructured input stream of data instances from the computing environment, the unstructured input stream being time stamped; categorizing the data instances of the unstructured input stream of data instances, the data instances comprising at least one principle value and a set of categorical attributes determined through machine learning; generating anomaly scores for each of the data instances collected over a period of time; and detecting a change in the categorical attribute that is indicative of an anomaly.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>ELECTRICITY</subject><subject>PHYSICS</subject><subject>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLB09PP3dfSJVHD0c1FwdgwNdgzx9PdTcHENcXUGszz9FJz9fQNCQzz93BVc_cI8g_z9fF39QoJ5GFjTEnOKU3mhNDeDgptriLOHbmpBfnxqcUFicmpeakm8a4CxmaGZgaWZo4kxEUoAVRooqw</recordid><startdate>20201014</startdate><enddate>20201014</enddate><creator>DODSON, Stephen</creator><creator>ROBERTS, David Mark</creator><creator>VEASEY, Thomas</creator><scope>EVB</scope></search><sort><creationdate>20201014</creationdate><title>ANOMALY AND CAUSATION DETECTION IN COMPUTING ENVIRONMENTS</title><author>DODSON, Stephen ; ROBERTS, David Mark ; VEASEY, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3616096A43</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>DODSON, Stephen</creatorcontrib><creatorcontrib>ROBERTS, David Mark</creatorcontrib><creatorcontrib>VEASEY, Thomas</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>DODSON, Stephen</au><au>ROBERTS, David Mark</au><au>VEASEY, Thomas</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>ANOMALY AND CAUSATION DETECTION IN COMPUTING ENVIRONMENTS</title><date>2020-10-14</date><risdate>2020</risdate><abstract>Anomaly detection in computing environments is disclosed herein. An example method includes receiving an unstructured input stream of data instances from the computing environment, the unstructured input stream being time stamped; categorizing the data instances of the unstructured input stream of data instances, the data instances comprising at least one principle value and a set of categorical attributes determined through machine learning; generating anomaly scores for each of the data instances collected over a period of time; and detecting a change in the categorical attribute that is indicative of an anomaly.</abstract><oa>free_for_read</oa></addata></record> |
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| language | eng ; fre ; ger |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | ANOMALY AND CAUSATION DETECTION IN COMPUTING ENVIRONMENTS |
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