{"id":"https://openalex.org/W3029564598","doi":"https://doi.org/10.1145/3318464.3380579","title":"Learning Multi-Dimensional Indexes","display_name":"Learning Multi-Dimensional Indexes","publication_year":2020,"publication_date":"2020-05-29","ids":{"openalex":"https://openalex.org/W3029564598","doi":"https://doi.org/10.1145/3318464.3380579","mag":"3029564598"},"language":"en","primary_location":{"id":"doi:10.1145/3318464.3380579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3318464.3380579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3318464.3380579","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3318464.3380579","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Vikram Nathan","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vikram Nathan","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jialin Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jialin Ding","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mohammad Alizadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Alizadeh","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tim Kraska","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tim Kraska","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.5346,"has_fulltext":true,"cited_by_count":168,"citation_normalized_percentile":{"value":0.99354292,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"985","last_page":"1000"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.9991999864578247,"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/T11478","display_name":"Caching and Content Delivery","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.8159000277519226},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6126000285148621},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.604200005531311},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5497000217437744},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5164999961853027},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5065000057220459},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43779999017715454}],"concepts":[{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.8159000277519226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7580000162124634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6258999705314636},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.604200005531311},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3547999858856201},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C59276292","wikidata":"https://www.wikidata.org/wiki/Q580427","display_name":"Database index","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2700999975204468},{"id":"https://openalex.org/C41431624","wikidata":"https://www.wikidata.org/wiki/Q1053357","display_name":"Block size","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3318464.3380579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3318464.3380579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3318464.3380579","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1912.01668","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.01668","pdf_url":"https://arxiv.org/pdf/1912.01668","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"}],"best_oa_location":{"id":"doi:10.1145/3318464.3380579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3318464.3380579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3318464.3380579","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1715532879","display_name":"CAREER: Data-Driven Network Resource Management Systems","funder_award_id":"1751009","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2162084259","display_name":null,"funder_award_id":"CNS-1751009","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2693312934","display_name":null,"funder_award_id":"16-43-D3M-FP040","funder_id":"https://openalex.org/F4320337531","funder_display_name":"Defense Sciences Office, DARPA"},{"id":"https://openalex.org/G3282283043","display_name":null,"funder_award_id":"16-43-D3M-FP040","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G5245462483","display_name":null,"funder_award_id":"IIS-1900933, CNS-1751009","funder_id":"https://openalex.org/F4320309856","funder_display_name":"National Youth Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308943","display_name":"Microsoft Research","ror":"https://ror.org/00d0nc645"},{"id":"https://openalex.org/F4320309856","display_name":"National Youth Science Foundation","ror":"https://ror.org/054yz2f06"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320337531","display_name":"Defense Sciences Office, DARPA","ror":"https://ror.org/0447fe631"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3029564598.pdf","grobid_xml":"https://content.openalex.org/works/W3029564598.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W1981420413","https://openalex.org/W2035419547","https://openalex.org/W2168503413","https://openalex.org/W2752313434","https://openalex.org/W2923407516"],"related_works":[],"abstract_inverted_index":{"Scanning":[0],"and":[1,51,59,80,88,117],"filtering":[2],"over":[3,26],"multi-dimensional":[4,31,68,109],"tables":[5],"are":[6,46],"key":[7],"operations":[8],"in":[9],"modern":[10],"analytical":[11],"database":[12,130],"engines.":[13],"To":[14],"optimize":[15],"the":[16,85],"performance":[17,53,101],"of":[18,98],"these":[19,44],"operations,":[20],"databases":[21],"often":[22,47],"create":[23],"clustered":[24],"indexes":[25,32,110],"a":[27,67,77,123],"single":[28],"dimension":[29],"or":[30,36,111],"such":[33],"as":[34,122],"R-Trees,":[35],"use":[37],"complex":[38],"sort":[39,112],"orders":[40,97,113],"(e.g.,":[41],"Z-ordering).":[42],"However,":[43],"schemes":[45],"hard":[48],"to":[49,76,95],"tune":[50],"their":[52],"is":[54],"inconsistent":[55],"across":[56],"different":[57],"datasets":[58,116],"queries.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"introduce":[65],"Flood,":[66],"in-memory":[69],"read-optimized":[70],"index":[71,86],"that":[72],"automatically":[73],"adapts":[74],"itself":[75],"particular":[78],"dataset":[79],"workload":[81],"by":[82],"jointly":[83],"optimizing":[84],"structure":[87],"data":[89],"storage":[90],"layout.":[91],"Flood":[92],"achieves":[93],"up":[94],"three":[96],"magnitude":[99],"faster":[100],"for":[102],"range":[103],"scans":[104],"with":[105],"predicates":[106],"than":[107],"state-of-the-art":[108],"on":[114],"real-world":[115],"workloads.":[118],"Our":[119],"work":[120],"serves":[121],"building":[124],"block":[125],"towards":[126],"an":[127],"end-to-end":[128],"learned":[129],"system.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":39},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":9}],"updated_date":"2026-06-18T10:00:31.954636","created_date":"2020-06-05T00:00:00"}