{"id":"https://openalex.org/W2784237011","doi":"https://doi.org/10.1109/bigdata.2017.8258110","title":"Designing a high performance cluster for large-scale SQL-on-hadoop analytics","display_name":"Designing a high performance cluster for large-scale SQL-on-hadoop analytics","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2784237011","doi":"https://doi.org/10.1109/bigdata.2017.8258110","mag":"2784237011"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5091370637","display_name":"Ajay Dholakia","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ajay Dholakia","raw_affiliation_strings":["Lenovo, Morrisville, NC, USA"],"affiliations":[{"raw_affiliation_string":"Lenovo, Morrisville, NC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002893863","display_name":"Prasad Venkatachar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prasad Venkatachar","raw_affiliation_strings":["Lenovo, Morrisville, NC, USA"],"affiliations":[{"raw_affiliation_string":"Lenovo, Morrisville, NC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016173449","display_name":"Kshitij Doshi","orcid":"https://orcid.org/0000-0002-1927-3995"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kshitij Doshi","raw_affiliation_strings":["Intel, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Intel, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074981958","display_name":"Ravikanth Durgavajhala","orcid":null},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ravikanth Durgavajhala","raw_affiliation_strings":["Intel, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Intel, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088653695","display_name":"Stewart Tate","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stewart Tate","raw_affiliation_strings":["IBM, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110113360","display_name":"Berni Schiefer","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Berni Schiefer","raw_affiliation_strings":["IBM, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081118590","display_name":"Matthew Sheard","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088690","display_name":"Mellanox Technologies (United States)","ror":"https://ror.org/006zwjq84","country_code":"US","type":"company","lineage":["https://openalex.org/I4210088690"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Sheard","raw_affiliation_strings":["Mellanox Technologies, Inc., Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Mellanox Technologies, Inc., Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210088690"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088457837","display_name":"Ramnath Sai Sagar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088690","display_name":"Mellanox Technologies (United States)","ror":"https://ror.org/006zwjq84","country_code":"US","type":"company","lineage":["https://openalex.org/I4210088690"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramnath Sai Sagar","raw_affiliation_strings":["Mellanox Technologies, Inc., Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Mellanox Technologies, Inc., Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210088690"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5091370637"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9673,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84182759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1701","last_page":"1703"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11719","display_name":"Data Quality and Management","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8166836500167847},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.7603616118431091},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.7404922246932983},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.6663492321968079},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.5968237519264221},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5015294551849365},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4706365764141083},{"id":"https://openalex.org/keywords/data-warehouse","display_name":"Data warehouse","score":0.4660924971103668},{"id":"https://openalex.org/keywords/in-memory-processing","display_name":"In-Memory Processing","score":0.4647146463394165},{"id":"https://openalex.org/keywords/data-transformation-services","display_name":"Data Transformation Services","score":0.44047585129737854},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.4355657398700714},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.26365402340888977},{"id":"https://openalex.org/keywords/query-by-example","display_name":"Query by Example","score":0.21507641673088074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1850840151309967},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1465345025062561}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8166836500167847},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.7603616118431091},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.7404922246932983},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.6663492321968079},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.5968237519264221},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5015294551849365},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4706365764141083},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.4660924971103668},{"id":"https://openalex.org/C123593499","wikidata":"https://www.wikidata.org/wiki/Q6008583","display_name":"In-Memory Processing","level":5,"score":0.4647146463394165},{"id":"https://openalex.org/C141589383","wikidata":"https://www.wikidata.org/wiki/Q644775","display_name":"Data Transformation Services","level":5,"score":0.44047585129737854},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.4355657398700714},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.26365402340888977},{"id":"https://openalex.org/C194222762","wikidata":"https://www.wikidata.org/wiki/Q114486","display_name":"Query by Example","level":4,"score":0.21507641673088074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1850840151309967},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1465345025062561},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.0},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6600000262260437,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2106625078","https://openalex.org/W2584041701","https://openalex.org/W4255327858"],"related_works":["https://openalex.org/W2748564340","https://openalex.org/W4313062323","https://openalex.org/W637012668","https://openalex.org/W101460764","https://openalex.org/W2379872950","https://openalex.org/W2353943211","https://openalex.org/W2267922067","https://openalex.org/W4382751403","https://openalex.org/W1526644996","https://openalex.org/W4237526087"],"abstract_inverted_index":{"Executing":[0],"and":[1,8,17,79,124,131],"optimizing":[2],"SQL":[3,24,121],"analytics":[4,25,57],"on":[5,26],"Data":[6,10],"Lakes":[7],"Enterprise":[9],"Warehouses":[11],"(EDW)":[12],"are":[13],"areas":[14],"of":[15,92,103,114],"significant":[16],"growing":[18],"interest.":[19],"Achieving":[20],"high":[21],"performance":[22],"for":[23,34,49,97],"large-scale":[27,115],"data":[28,35,70],"repositories":[29],"remains":[30],"a":[31,84],"key":[32],"challenge":[33],"practitioners.":[36],"The":[37],"SQL-on-Hadoop":[38,116],"Analytics":[39],"solution":[40],"described":[41],"in":[42],"this":[43],"paper":[44],"is":[45],"very":[46],"well":[47],"suited":[48],"implementing":[50],"the":[51,94,98,101,119],"infrastructure":[52],"to":[53,89],"support":[54],"these":[55],"modern":[56],"initiatives":[58],"while":[59],"meeting":[60],"requirements":[61],"such":[62,105],"as":[63,118],"higher":[64],"performance,":[65],"lower":[66,73],"cost,":[67],"more":[68],"efficient":[69],"center":[71],"footprint,":[72],"power":[74],"consumption,":[75],"appropriate":[76],"storage":[77],"needs":[78],"increased":[80],"reliability.":[81],"By":[82],"using":[83],"TPC-DS":[85],"derived":[86],"workload":[87],"applied":[88],"100":[90],"TB":[91],"data,":[93],"work":[95],"demonstrates":[96],"first":[99],"time":[100],"feasibility":[102],"designing":[104],"an":[106],"extremely":[107],"high-performance":[108],"cluster.":[109],"Furthermore,":[110],"it":[111],"enables":[112,125],"investigation":[113],"systems":[117],"Spark":[120,133],"framework":[122],"matures":[123],"similar":[126],"investigations":[127],"into":[128],"machine":[129],"learning":[130],"related":[132],"capabilities.":[134]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
