{"id":"https://openalex.org/W4229050510","doi":"https://doi.org/10.1007/s41019-022-00186-4","title":"Dynamic Index Construction with Deep Reinforcement Learning","display_name":"Dynamic Index Construction with Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-05-06","ids":{"openalex":"https://openalex.org/W4229050510","doi":"https://doi.org/10.1007/s41019-022-00186-4"},"language":"en","primary_location":{"id":"doi:10.1007/s41019-022-00186-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-022-00186-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-022-00186-4.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s41019-022-00186-4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064646829","display_name":"Sai Wu","orcid":"https://orcid.org/0000-0002-1866-9197"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sai Wu","raw_affiliation_strings":["College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-1866-9197","affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100414362","display_name":"Ying Li","orcid":"https://orcid.org/0000-0002-9604-2664"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Li","raw_affiliation_strings":["Netease(Hangzhou) Network Co., Ltd., Hangzhou, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Netease(Hangzhou) Network Co., Ltd., Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012539121","display_name":"Haoqi Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoqi Zhu","raw_affiliation_strings":["Netease(Hangzhou) Network Co., Ltd., Hangzhou, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Netease(Hangzhou) Network Co., Ltd., Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034520734","display_name":"Junbo Zhao","orcid":"https://orcid.org/0000-0002-3637-2936"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbo Zhao","raw_affiliation_strings":["College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100389286","display_name":"Gang Chen","orcid":"https://orcid.org/0000-0002-7483-0045"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Chen","raw_affiliation_strings":["College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064646829"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":3.4222,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.93037025,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"7","issue":"2","first_page":"87","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9962999820709229,"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":0.9962999820709229,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9725000262260437,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8958829641342163},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8403463363647461},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.6256005167961121},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6234108805656433},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5395054817199707},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5104295611381531},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.4893152415752411},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46492815017700195},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4225737452507019},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4175289273262024},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4142375886440277}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8958829641342163},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8403463363647461},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.6256005167961121},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6234108805656433},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5395054817199707},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5104295611381531},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.4893152415752411},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46492815017700195},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4225737452507019},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4175289273262024},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4142375886440277},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s41019-022-00186-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-022-00186-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-022-00186-4.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s41019-022-00186-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-022-00186-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-022-00186-4.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3820517054","display_name":null,"funder_award_id":"2021C01109","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"}],"funders":[{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4229050510.pdf","grobid_xml":"https://content.openalex.org/works/W4229050510.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1604088952","https://openalex.org/W2030062409","https://openalex.org/W2064675550","https://openalex.org/W2151224499","https://openalex.org/W2798418112","https://openalex.org/W2889503624","https://openalex.org/W2945486614","https://openalex.org/W2946026089","https://openalex.org/W2948233700","https://openalex.org/W2948513753","https://openalex.org/W2962771342","https://openalex.org/W2963355447","https://openalex.org/W2979531022","https://openalex.org/W2998249308","https://openalex.org/W3029535034","https://openalex.org/W3030149280","https://openalex.org/W3033065823","https://openalex.org/W3094011786","https://openalex.org/W3096737792","https://openalex.org/W3100925961","https://openalex.org/W3103567827","https://openalex.org/W3121516856","https://openalex.org/W6637681330","https://openalex.org/W6814003322"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843","https://openalex.org/W3208304128"],"abstract_inverted_index":{"Abstract":[0],"Thanks":[1],"to":[2,107,111,116],"the":[3,23,28,40,47,58,77,82,99,124,130,140,147,153,157,166],"rapid":[4],"advances":[5],"in":[6,31,76,91,139],"artificial":[7],"intelligence,":[8],"a":[9,53,63,113,118],"brand":[10],"new":[11],"venue":[12],"for":[13],"database":[14],"performance":[15,89,167],"optimization":[16],"is":[17,39,110],"through":[18],"deep":[19],"neural":[20],"networks":[21],"and":[22,36,65,85,162],"reinforcement":[24],"learning":[25,149],"paradigm.":[26],"Alongside":[27],"long":[29],"literature":[30],"this":[32,45,68,95,108],"regime,":[33],"an":[34],"iconic":[35],"crucial":[37],"problem":[38],"index":[41,126,137,160,171],"structure":[42,138],"building.":[43],"For":[44],"problem,":[46],"prior":[48],"works":[49],"have":[50],"largely":[51],"adopted":[52],"pure":[54,148],"learning-based":[55],"solution":[56],"replacing":[57],"traditional":[59],"methods":[60],"such":[61],"as":[62],"B-tree":[64],"Hashing.":[66],"While":[67],"line":[69],"of":[70],"research":[71],"has":[72],"drawn":[73],"much":[74],"attention":[75],"field,":[78],"they":[79],"ubiquitously":[80],"abandon":[81],"semantic":[83],"guarantees":[84],"also":[86],"suffer":[87],"from":[88,168],"loss":[90],"certain":[92],"scenarios.":[93],"In":[94],"work,":[96],"we":[97],"propose":[98],"Neural":[100],"Index":[101],"Search":[102],"(NIS)":[103],"framework.":[104],"The":[105],"core":[106],"framework":[109,179],"train":[112],"search":[114],"policy":[115],"find":[117],"near":[119],"optimal":[120],"combination":[121],"plan":[122],"over":[123],"existing":[125],"structures,":[127],"together":[128],"with":[129,135],"required":[131],"configuration":[132],"parameters":[133],"associated":[134],"each":[136],"plan.":[141],"We":[142],"argue":[143],"that":[144,177],"compared":[145],"against":[146],"approaches,":[150],"NIS":[151],"enjoys":[152],"advantages":[154],"brought":[155],"by":[156],"chosen":[158],"conventional":[159],"structures":[161],"further":[163],"robustly":[164],"enhances":[165],"any":[169],"singular":[170],"structure.":[172],"Extensive":[173],"empirical":[174],"results":[175],"demonstrate":[176],"our":[178],"achieves":[180],"state-of-the-art":[181],"performances":[182],"on":[183],"several":[184],"benchmarks.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
