{"id":"https://openalex.org/W4205145150","doi":"https://doi.org/10.1109/bigdata52589.2021.9671617","title":"Hadoop-MTA: a system for Multi Data-center Trillion Concepts Auto-ML atop Hadoop","display_name":"Hadoop-MTA: a system for Multi Data-center Trillion Concepts Auto-ML atop Hadoop","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205145150","doi":"https://doi.org/10.1109/bigdata52589.2021.9671617"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671617","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049950372","display_name":"Keqian Li","orcid":"https://orcid.org/0009-0002-5956-3038"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keqian Li","raw_affiliation_strings":["Yahoo Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100708726","display_name":"Yifan Hu","orcid":"https://orcid.org/0000-0003-2017-924X"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifan Hu","raw_affiliation_strings":["Yahoo Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101503356","display_name":"Manisha Verma","orcid":"https://orcid.org/0000-0001-5281-2527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manisha Verma","raw_affiliation_strings":["Amazon Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100740041","display_name":"Fei Tan","orcid":"https://orcid.org/0000-0002-3232-1912"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Tan","raw_affiliation_strings":["Yahoo Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059690482","display_name":"Changwei Hu","orcid":"https://orcid.org/0000-0001-7511-0739"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Changwei Hu","raw_affiliation_strings":["XPeng Motors"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"XPeng Motors","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051176330","display_name":"Tejaswi Kasturi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tejaswi Kasturi","raw_affiliation_strings":["RED"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RED","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073639119","display_name":"Kevin Yen","orcid":"https://orcid.org/0000-0002-4338-9680"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kevin Yen","raw_affiliation_strings":["RED"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RED","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"5953","last_page":"5955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7896324396133423},{"id":"https://openalex.org/keywords/data-center","display_name":"Data center","score":0.6172763109207153},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5679831504821777},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5557434558868408},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.49452754855155945},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4719851613044739},{"id":"https://openalex.org/keywords/distributed-database","display_name":"Distributed database","score":0.4540884494781494},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4449112117290497},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4349902272224426},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.4340442419052124},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38586926460266113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27816832065582275},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.2059454619884491},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11534807085990906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896324396133423},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.6172763109207153},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5679831504821777},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5557434558868408},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.49452754855155945},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4719851613044739},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.4540884494781494},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4449112117290497},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4349902272224426},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.4340442419052124},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38586926460266113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27816832065582275},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.2059454619884491},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11534807085990906},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671617","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W410850256","https://openalex.org/W2109865831","https://openalex.org/W2626982577","https://openalex.org/W2802181385","https://openalex.org/W2906809452","https://openalex.org/W2953363137","https://openalex.org/W3003703817","https://openalex.org/W3101785758","https://openalex.org/W3114303065","https://openalex.org/W3209406482","https://openalex.org/W4230539123","https://openalex.org/W6614148910"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4296209631","https://openalex.org/W4200136508"],"abstract_inverted_index":{"The":[0],"ever-growing":[1],"computation":[2,22,127],"capability":[3],"distributed":[4,25,96,126,158],"infrastructure":[5,176],"brings":[6,28],"tremendous":[7],"opportunities":[8],"for":[9,33,115],"mining":[10],"and":[11,30,47,74,86,137,161,184,189],"analysis":[12],"of":[13,24,39,45,50,66,77,123,155,181],"data":[14,40,62,72],"that":[15,129,149],"was":[16],"impossible":[17],"otherwise.":[18],"Meanwhile,":[19],"the":[20,37,42,48,55,64,75,93,105,124,156],"inherent":[21],"model":[23,169],"system":[26,114],"also":[27],"unique":[29,182],"non-trivial":[31],"challenges":[32],"traditional":[34],"Auto-ML,":[35],"including":[36],"explosion":[38],"dimensions,":[41],"expected":[43],"absence":[44],"features,":[46],"heterogeneity":[49],"information.":[51],"This":[52],"is":[53,170],"especially":[54],"case":[56],"in":[57,63,70,84,104,177],"modern":[58],"Internet":[59],"enterprises,":[60],"where":[61],"scale":[65,95,145,166],"trillions":[67],"are":[68],"stored":[69],"multiple":[71,143],"centers,":[73],"discovery":[76],"subtle":[78],"signals":[79],"could":[80],"incur":[81],"significant":[82],"impact":[83],"revenue":[85],"welfare.":[87],"How":[88],"can":[89],"we":[90,110,147],"best":[91],"harness":[92],"large":[94,144],"machine":[97],"learning,":[98],"but":[99],"without":[100],"keeping":[101],"engineers":[102],"constantly":[103],"loop?":[106],"In":[107],"this":[108],"work,":[109],"present":[111],"Hadoop-MTA,":[112],"a":[113],"Multi":[116],"Data-center,":[117],"Trillion":[118],"Concepts,":[119],"Auto-ML":[120],"on":[121,197],"top":[122],"Hadoop":[125,174],"environment":[128],"leverages":[130],"sparsity":[131],"aware":[132],"heterogeneous":[133],"knowledge":[134],"graph":[135],"representation":[136],"dimensionality":[138],"agnostic":[139],"parallel":[140],"learning.":[141],"Through":[142],"experiments,":[146],"find":[148],"Hadoop-MTA":[150],"significantly":[151],"output-performs":[152],"competitive":[153],"state":[154],"art":[157],"learning":[159],"algorithms":[160],"scales":[162],"well":[163],"to":[164,173],"trillion":[165],"data-sets.":[167],"Our":[168],"rolled":[171],"out":[172],"serving":[175],"Yahoo":[178],"covering":[179],"billions":[180],"identities":[183],"shows":[185],"improvements":[186],"129.5%":[187],"accuracy":[188],"106.5":[190],"%":[191],"weighted":[192],"F1-score":[193],"(more":[194],"than":[195],"2x)":[196],"key":[198],"targeting":[199],"use":[200],"cases.":[201]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
