{"id":"https://openalex.org/W2963745734","doi":"https://doi.org/10.1109/bigdata.2018.8622018","title":"Learning-based Automatic Parameter Tuning for Big Data Analytics Frameworks","display_name":"Learning-based Automatic Parameter Tuning for Big Data Analytics Frameworks","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2963745734","doi":"https://doi.org/10.1109/bigdata.2018.8622018","mag":"2963745734"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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/A5018729096","display_name":"Liang Bao","orcid":"https://orcid.org/0000-0003-1710-4335"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Bao","raw_affiliation_strings":["School of Computer Science and Technology, XiDian University, Xi\u2019an, Shaan Xi, China","School of Computer Science and Technology, XiDian University, Xi'an, Shaan Xi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, XiDian University, Xi\u2019an, Shaan Xi, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Computer Science and Technology, XiDian University, Xi'an, Shaan Xi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100352241","display_name":"Xin Liu","orcid":"https://orcid.org/0000-0002-5379-8269"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Liu","raw_affiliation_strings":["Department of Computer Science, University of California, Davis, California, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, Davis, California, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045347651","display_name":"Weizhao Chen","orcid":"https://orcid.org/0000-0003-2482-1906"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weizhao Chen","raw_affiliation_strings":["School of Computer Science and Technology, XiDian University, Xi\u2019an, Shaan Xi, China","School of Computer Science and Technology, XiDian University, Xi'an, Shaan Xi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, XiDian University, Xi\u2019an, Shaan Xi, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Computer Science and Technology, XiDian University, Xi'an, Shaan Xi, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018729096"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":2.2802,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.91194548,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"181","last_page":"190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9991000294685364,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9991000294685364,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9976000189781189,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9943000078201294,"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":0.8232890367507935},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.8045985102653503},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.7621577978134155},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6901442408561707},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5916600823402405},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5897701382637024},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5789322853088379},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5386165976524353},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5222437977790833},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.42390862107276917},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40243592858314514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8232890367507935},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.8045985102653503},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.7621577978134155},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6901442408561707},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5916600823402405},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5897701382637024},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5789322853088379},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5386165976524353},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5222437977790833},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.42390862107276917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40243592858314514},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W136979389","https://openalex.org/W1582576952","https://openalex.org/W1834532152","https://openalex.org/W1842268893","https://openalex.org/W1980376525","https://openalex.org/W2038361169","https://openalex.org/W2038669746","https://openalex.org/W2059878010","https://openalex.org/W2069870183","https://openalex.org/W2086966678","https://openalex.org/W2097998348","https://openalex.org/W2103434431","https://openalex.org/W2114896543","https://openalex.org/W2131975293","https://openalex.org/W2155072926","https://openalex.org/W2170997892","https://openalex.org/W2173213060","https://openalex.org/W2175075972","https://openalex.org/W2189149359","https://openalex.org/W2249475272","https://openalex.org/W2296061154","https://openalex.org/W2309679942","https://openalex.org/W2318383848","https://openalex.org/W2582789036","https://openalex.org/W2616703129","https://openalex.org/W2735694356","https://openalex.org/W2739485145","https://openalex.org/W2760770811","https://openalex.org/W3098844916","https://openalex.org/W3101748252","https://openalex.org/W4239013215","https://openalex.org/W4252793640","https://openalex.org/W6602526659","https://openalex.org/W6605496753","https://openalex.org/W6638433704","https://openalex.org/W6639007902","https://openalex.org/W6660254288","https://openalex.org/W6674385629","https://openalex.org/W6679815717","https://openalex.org/W6697145832","https://openalex.org/W6697698479"],"related_works":["https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W3003320923","https://openalex.org/W2106140982","https://openalex.org/W2766461310","https://openalex.org/W4247566972","https://openalex.org/W4388692845","https://openalex.org/W3202731209","https://openalex.org/W3211874991","https://openalex.org/W3191926225"],"abstract_inverted_index":{"Big":[0],"data":[1,10],"analytics":[2],"frameworks":[3,14],"(BDAFs)":[4],"have":[5,41],"been":[6,42],"widely":[7],"used":[8],"for":[9,133],"processing":[11],"applications.":[12],"These":[13],"provide":[15,122],"a":[16,27,47,55,60,85,102,107,116,123,156],"large":[17],"number":[18],"of":[19,118],"configuration":[20],"parameters":[21],"to":[22,26,50,75],"users,":[23],"which":[24],"leads":[25],"tuning":[28,39,71,178],"issue":[29],"that":[30,73,93,120,163],"overwhelms":[31],"users.":[32],"To":[33],"address":[34],"this":[35,64],"issue,":[36],"many":[37],"automatic":[38,69],"approaches":[40],"proposed.":[43],"However,":[44],"it":[45,94],"remains":[46],"critical":[48],"challenge":[49],"generate":[51,96],"enough":[52],"samples":[53,119],"in":[54],"high-dimensional":[56,128],"parameter":[57,70,129],"space":[58],"within":[59],"time":[61,79,109],"constraint.":[62,110],"In":[63],"paper,":[65],"we":[66],"present":[67],"AutoTune-an":[68],"system":[72,91],"aims":[74],"optimize":[76],"application":[77],"execution":[78],"on":[80,155,166,171,174],"BDAFs.":[81],"AutoTune":[82,113,142,164],"first":[83],"constructs":[84],"smaller-scale":[86],"testbed":[87],"from":[88],"the":[89,112,127,138,148,175],"production":[90],"so":[92],"can":[95,121],"more":[97,134],"samples,":[98],"and":[99,131,145,151,173],"thus":[100],"train":[101],"better":[103],"prediction":[104,140],"model,":[105],"under":[106],"given":[108],"Furthermore,":[111],"algorithm":[114],"produces":[115],"set":[117],"wide":[124],"coverage":[125],"over":[126],"space,":[130],"searches":[132],"promising":[135],"configurations":[136,168],"using":[137,147],"trained":[139],"model.":[141],"is":[143],"implemented":[144],"evaluated":[146],"Spark":[149],"framework":[150],"HiBench":[152],"benchmark":[153],"deployed":[154],"public":[157],"cloud.":[158],"Extensive":[159],"experimental":[160],"results":[161],"illustrate":[162],"improves":[165],"default":[167],"by":[169,180],"63.70%":[170],"average,":[172],"five":[176],"state-of-the-art":[177],"algorithms":[179],"6%-23%.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}