Machine learning system flow authoring tool
Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be reference...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| 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 | Mehanna, Hussein Mohamed Hassan Paton, James Robert Xie, Xiaowen Bowers, Stuart Michael Farnham, Rodrigo Bouchardet Dunn, Jeffrey Scott Sidorov, Aleksandr Gusatti Azzolini, Alisson Vagata, Pamela Shen |
| description | Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US10643144B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US10643144B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US10643144B23</originalsourceid><addsrcrecordid>eNrjZND2TUzOyMxLVchJTSzKy8xLVyiuLC5JzVVIy8kvV0gsLcnILwKJluTn5_AwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUkvjQYEMDMxNjQxMTJyNjYtQAAH0pKcU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Machine learning system flow authoring tool</title><source>esp@cenet</source><creator>Mehanna, Hussein Mohamed Hassan ; Paton, James Robert ; Xie, Xiaowen ; Bowers, Stuart Michael ; Farnham, Rodrigo Bouchardet ; Dunn, Jeffrey Scott ; Sidorov, Aleksandr ; Gusatti Azzolini, Alisson ; Vagata, Pamela Shen</creator><creatorcontrib>Mehanna, Hussein Mohamed Hassan ; Paton, James Robert ; Xie, Xiaowen ; Bowers, Stuart Michael ; Farnham, Rodrigo Bouchardet ; Dunn, Jeffrey Scott ; Sidorov, Aleksandr ; Gusatti Azzolini, Alisson ; Vagata, Pamela Shen</creatorcontrib><description>Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; 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=20200505&DB=EPODOC&CC=US&NR=10643144B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200505&DB=EPODOC&CC=US&NR=10643144B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Mehanna, Hussein Mohamed Hassan</creatorcontrib><creatorcontrib>Paton, James Robert</creatorcontrib><creatorcontrib>Xie, Xiaowen</creatorcontrib><creatorcontrib>Bowers, Stuart Michael</creatorcontrib><creatorcontrib>Farnham, Rodrigo Bouchardet</creatorcontrib><creatorcontrib>Dunn, Jeffrey Scott</creatorcontrib><creatorcontrib>Sidorov, Aleksandr</creatorcontrib><creatorcontrib>Gusatti Azzolini, Alisson</creatorcontrib><creatorcontrib>Vagata, Pamela Shen</creatorcontrib><title>Machine learning system flow authoring tool</title><description>Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</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>eNrjZND2TUzOyMxLVchJTSzKy8xLVyiuLC5JzVVIy8kvV0gsLcnILwKJluTn5_AwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUkvjQYEMDMxNjQxMTJyNjYtQAAH0pKcU</recordid><startdate>20200505</startdate><enddate>20200505</enddate><creator>Mehanna, Hussein Mohamed Hassan</creator><creator>Paton, James Robert</creator><creator>Xie, Xiaowen</creator><creator>Bowers, Stuart Michael</creator><creator>Farnham, Rodrigo Bouchardet</creator><creator>Dunn, Jeffrey Scott</creator><creator>Sidorov, Aleksandr</creator><creator>Gusatti Azzolini, Alisson</creator><creator>Vagata, Pamela Shen</creator><scope>EVB</scope></search><sort><creationdate>20200505</creationdate><title>Machine learning system flow authoring tool</title><author>Mehanna, Hussein Mohamed Hassan ; Paton, James Robert ; Xie, Xiaowen ; Bowers, Stuart Michael ; Farnham, Rodrigo Bouchardet ; Dunn, Jeffrey Scott ; Sidorov, Aleksandr ; Gusatti Azzolini, Alisson ; Vagata, Pamela Shen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US10643144B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Mehanna, Hussein Mohamed Hassan</creatorcontrib><creatorcontrib>Paton, James Robert</creatorcontrib><creatorcontrib>Xie, Xiaowen</creatorcontrib><creatorcontrib>Bowers, Stuart Michael</creatorcontrib><creatorcontrib>Farnham, Rodrigo Bouchardet</creatorcontrib><creatorcontrib>Dunn, Jeffrey Scott</creatorcontrib><creatorcontrib>Sidorov, Aleksandr</creatorcontrib><creatorcontrib>Gusatti Azzolini, Alisson</creatorcontrib><creatorcontrib>Vagata, Pamela Shen</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mehanna, Hussein Mohamed Hassan</au><au>Paton, James Robert</au><au>Xie, Xiaowen</au><au>Bowers, Stuart Michael</au><au>Farnham, Rodrigo Bouchardet</au><au>Dunn, Jeffrey Scott</au><au>Sidorov, Aleksandr</au><au>Gusatti Azzolini, Alisson</au><au>Vagata, Pamela Shen</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Machine learning system flow authoring tool</title><date>2020-05-05</date><risdate>2020</risdate><abstract>Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US10643144B2 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Machine learning system flow authoring tool |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T11%3A35%3A13IST&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=Mehanna,%20Hussein%20Mohamed%20Hassan&rft.date=2020-05-05&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS10643144B2%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 |