MANAGING DATA INGESTION AND STORAGE

An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of da...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Kabra, Namit, Saillet, Yannick, Gupta, Neerju
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Kabra, Namit
Saillet, Yannick
Gupta, Neerju
description An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of data in the subject datasets and automatically match the shallow term assignments for each row and column with a stored set of ranked terms, and automatically flag rows or columns matching with ranked terms above a predetermined threshold ranking for further analysis. The embodiment may automatically and continuously monitor and detect irrelevant metadata types to prevent subsequent analysis and storage of data including the irrelevant metadata types. The embodiment may automatically generate a criticality ranking for stored analysis datasets. The embodiment may detect low priority analysis datasets having a criticality ranking below a criticality threshold, and automatically place the low priority analysis datasets into cold storage.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2024078241A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2024078241A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2024078241A13</originalsourceid><addsrcrecordid>eNrjZFD2dfRzdPf0c1dwcQxxVAAyXINDPP39FBz9XBSCQ_yDHN1deRhY0xJzilN5oTQ3g7Kba4izh25qQX58anFBYnJqXmpJfGiwkYGRiYG5hZGJoaOhMXGqAM0vI24</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MANAGING DATA INGESTION AND STORAGE</title><source>esp@cenet</source><creator>Kabra, Namit ; Saillet, Yannick ; Gupta, Neerju</creator><creatorcontrib>Kabra, Namit ; Saillet, Yannick ; Gupta, Neerju</creatorcontrib><description>An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of data in the subject datasets and automatically match the shallow term assignments for each row and column with a stored set of ranked terms, and automatically flag rows or columns matching with ranked terms above a predetermined threshold ranking for further analysis. The embodiment may automatically and continuously monitor and detect irrelevant metadata types to prevent subsequent analysis and storage of data including the irrelevant metadata types. The embodiment may automatically generate a criticality ranking for stored analysis datasets. The embodiment may detect low priority analysis datasets having a criticality ranking below a criticality threshold, and automatically place the low priority analysis datasets into cold storage.</description><language>eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</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&amp;date=20240307&amp;DB=EPODOC&amp;CC=US&amp;NR=2024078241A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240307&amp;DB=EPODOC&amp;CC=US&amp;NR=2024078241A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Kabra, Namit</creatorcontrib><creatorcontrib>Saillet, Yannick</creatorcontrib><creatorcontrib>Gupta, Neerju</creatorcontrib><title>MANAGING DATA INGESTION AND STORAGE</title><description>An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of data in the subject datasets and automatically match the shallow term assignments for each row and column with a stored set of ranked terms, and automatically flag rows or columns matching with ranked terms above a predetermined threshold ranking for further analysis. The embodiment may automatically and continuously monitor and detect irrelevant metadata types to prevent subsequent analysis and storage of data including the irrelevant metadata types. The embodiment may automatically generate a criticality ranking for stored analysis datasets. The embodiment may detect low priority analysis datasets having a criticality ranking below a criticality threshold, and automatically place the low priority analysis datasets into cold storage.</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>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFD2dfRzdPf0c1dwcQxxVAAyXINDPP39FBz9XBSCQ_yDHN1deRhY0xJzilN5oTQ3g7Kba4izh25qQX58anFBYnJqXmpJfGiwkYGRiYG5hZGJoaOhMXGqAM0vI24</recordid><startdate>20240307</startdate><enddate>20240307</enddate><creator>Kabra, Namit</creator><creator>Saillet, Yannick</creator><creator>Gupta, Neerju</creator><scope>EVB</scope></search><sort><creationdate>20240307</creationdate><title>MANAGING DATA INGESTION AND STORAGE</title><author>Kabra, Namit ; Saillet, Yannick ; Gupta, Neerju</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2024078241A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Kabra, Namit</creatorcontrib><creatorcontrib>Saillet, Yannick</creatorcontrib><creatorcontrib>Gupta, Neerju</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kabra, Namit</au><au>Saillet, Yannick</au><au>Gupta, Neerju</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MANAGING DATA INGESTION AND STORAGE</title><date>2024-03-07</date><risdate>2024</risdate><abstract>An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of data in the subject datasets and automatically match the shallow term assignments for each row and column with a stored set of ranked terms, and automatically flag rows or columns matching with ranked terms above a predetermined threshold ranking for further analysis. The embodiment may automatically and continuously monitor and detect irrelevant metadata types to prevent subsequent analysis and storage of data including the irrelevant metadata types. The embodiment may automatically generate a criticality ranking for stored analysis datasets. The embodiment may detect low priority analysis datasets having a criticality ranking below a criticality threshold, and automatically place the low priority analysis datasets into cold storage.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US2024078241A1
source esp@cenet
subjects CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title MANAGING DATA INGESTION AND STORAGE
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T08%3A28%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Kabra,%20Namit&rft.date=2024-03-07&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2024078241A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true