{"id":"https://openalex.org/W3099126479","doi":"https://doi.org/10.1145/3437984.3458830","title":"Towards a General Framework for ML-based Self-tuning Databases","display_name":"Towards a General Framework for ML-based Self-tuning Databases","publication_year":2021,"publication_date":"2021-04-25","ids":{"openalex":"https://openalex.org/W3099126479","doi":"https://doi.org/10.1145/3437984.3458830","mag":"3099126479"},"language":"en","primary_location":{"id":"doi:10.1145/3437984.3458830","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437984.3458830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2011.07921","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052543224","display_name":"Thomas Schmied","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Thomas Schmied","raw_affiliation_strings":["IBM Research - Zurich Switzerland","IBM Research - Zurich,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich Switzerland","institution_ids":["https://openalex.org/I4210126328"]},{"raw_affiliation_string":"IBM Research - Zurich,Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026981591","display_name":"Diego Didona","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Diego Didona","raw_affiliation_strings":["IBM Research - Zurich Switzerland","IBM Research - Zurich,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich Switzerland","institution_ids":["https://openalex.org/I4210126328"]},{"raw_affiliation_string":"IBM Research - Zurich,Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020736372","display_name":"Andreas D\u00f6ring","orcid":"https://orcid.org/0009-0004-2731-8892"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Andreas D\u00f6ring","raw_affiliation_strings":["IBM Research - Zurich Switzerland","IBM Research - Zurich,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich Switzerland","institution_ids":["https://openalex.org/I4210126328"]},{"raw_affiliation_string":"IBM Research - Zurich,Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040727953","display_name":"Thomas Parnell","orcid":"https://orcid.org/0000-0002-1308-6590"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Thomas Parnell","raw_affiliation_strings":["IBM Research - Zurich Switzerland","IBM Research - Zurich,Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich Switzerland","institution_ids":["https://openalex.org/I4210126328"]},{"raw_affiliation_string":"IBM Research - Zurich,Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054351615","display_name":"Nikolas Ioannou","orcid":null},"institutions":[{"id":"https://openalex.org/I1321014770","display_name":"Association for Computing Machinery","ror":"https://ror.org/03wsadn68","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1321014770"]},{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Nikolas Ioannou","raw_affiliation_strings":["IBM Research - Zurich Switzerland","Association for Computing Machinery"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich Switzerland","institution_ids":["https://openalex.org/I4210126328"]},{"raw_affiliation_string":"Association for Computing Machinery","institution_ids":["https://openalex.org/I1321014770"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6761,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60570322,"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":"24","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","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/T10462","display_name":"Reinforcement Learning in Robotics","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7674242258071899},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6702848076820374},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.6287249326705933},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5936819911003113},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5861124992370605},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.47112202644348145},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.457871675491333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4457795023918152},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44020605087280273},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.41029611229896545},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3840319812297821},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09014013409614563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7674242258071899},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6702848076820374},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.6287249326705933},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5936819911003113},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5861124992370605},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.47112202644348145},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.457871675491333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4457795023918152},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44020605087280273},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.41029611229896545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3840319812297821},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09014013409614563},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3437984.3458830","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437984.3458830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2011.07921","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.07921","pdf_url":"https://arxiv.org/pdf/2011.07921","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3099126479","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2011.07921.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2011.07921","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2011.07921","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2011.07921","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.07921","pdf_url":"https://arxiv.org/pdf/2011.07921","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3099126479.pdf","grobid_xml":"https://content.openalex.org/works/W3099126479.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2038669746","https://openalex.org/W2099201756","https://openalex.org/W2121863487","https://openalex.org/W2173248099","https://openalex.org/W2556522401","https://openalex.org/W2613206411","https://openalex.org/W2803453473","https://openalex.org/W2873705236","https://openalex.org/W2948513753","https://openalex.org/W2963815651","https://openalex.org/W2964515685","https://openalex.org/W2970048308","https://openalex.org/W2970562710","https://openalex.org/W2998993395","https://openalex.org/W3037599988","https://openalex.org/W3104807968","https://openalex.org/W3147327897","https://openalex.org/W4288310623"],"related_works":["https://openalex.org/W2970562710","https://openalex.org/W2789525339","https://openalex.org/W3192751261","https://openalex.org/W2605070055","https://openalex.org/W1742716246","https://openalex.org/W3131916193","https://openalex.org/W2186964559","https://openalex.org/W2106411961","https://openalex.org/W2804075420","https://openalex.org/W2152008486","https://openalex.org/W2971565477","https://openalex.org/W2749412269","https://openalex.org/W2731604420","https://openalex.org/W1258972194","https://openalex.org/W3130177876","https://openalex.org/W3173775287","https://openalex.org/W2056184385","https://openalex.org/W2886703017","https://openalex.org/W3041354530","https://openalex.org/W3047084461"],"abstract_inverted_index":{"Machine":[0],"learning":[1,26],"(ML)":[2],"methods":[3,38,147],"have":[4],"recently":[5],"emerged":[6],"as":[7,57],"an":[8],"effective":[9],"way":[10],"to":[11,39,93,156,190],"perform":[12],"automated":[13],"parameter":[14,67],"tuning":[15,118],"of":[16,61,66,96,135,152],"databases.":[17],"State-of-the-art":[18],"approaches":[19],"include":[20],"Bayesian":[21],"optimization":[22,196],"(BO)":[23],"and":[24,64,74,102,145],"reinforcement":[25],"(RL).":[27],"In":[28],"this":[29,46,127,186],"work,":[30],"we":[31,50,54,76,85,112,129],"describe":[32,51],"our":[33],"experience":[34],"when":[35,117],"applying":[36],"these":[37,80],"a":[40,90,161,166],"database":[41],"not":[42],"yet":[43],"studied":[44],"in":[45,71,100,126,185],"context:":[47],"FoundationDB.":[48],"Firstly,":[49],"the":[52,94,108,133,150],"challenges":[53],"faced,":[55],"such":[56],"unknown":[58],"valid":[59],"ranges":[60],"configuration":[62,167],"parameters":[63],"combinations":[65],"values":[68],"that":[69,87,168,182],"result":[70],"invalid":[72],"runs,":[73],"how":[75],"mitigated":[77],"them.":[78],"While":[79],"issues":[81],"are":[82,89],"typically":[83],"overlooked,":[84],"argue":[86],"they":[88],"crucial":[91],"barrier":[92],"adoption":[95],"ML":[97,121,178],"self-tuning":[98],"techniques":[99],"databases,":[101],"thus":[103],"deserve":[104],"more":[105,176,192],"attention":[106],"from":[107],"research":[109],"community.":[110],"Secondly,":[111],"present":[113],"experimental":[114],"results":[115,140],"obtained":[116],"FoundationDB":[119,153],"using":[120],"methods.":[122,179],"Unlike":[123],"prior":[124],"work":[125,184],"domain,":[128],"also":[130],"compare":[131],"with":[132],"simplest":[134],"baselines:":[136],"random":[137,158],"search.":[138],"Our":[139],"show":[141],"that,":[142],"while":[143],"BO":[144],"RL":[146],"can":[148],"improve":[149],"throughput":[151],"by":[154],"up":[155],"38%,":[157],"search":[159],"is":[160,169],"highly":[162],"competitive":[163],"baseline,":[164],"finding":[165],"only":[170],"4%":[171],"worse":[172],"than":[173],"the,":[174],"vastly":[175],"complex,":[177],"We":[180],"conclude":[181],"future":[183],"area":[187],"may":[188],"want":[189],"focus":[191],"on":[193],"randomized,":[194],"model-free":[195],"algorithms.":[197]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
