{"id":"https://openalex.org/W7117464323","doi":"https://doi.org/10.1109/ipccc66453.2025.11304630","title":"LASER: Line-Aware and Self-Balancing Learned Index for Rapid Key Lookup","display_name":"LASER: Line-Aware and Self-Balancing Learned Index for Rapid Key Lookup","publication_year":2025,"publication_date":"2025-11-15","ids":{"openalex":"https://openalex.org/W7117464323","doi":"https://doi.org/10.1109/ipccc66453.2025.11304630"},"language":null,"primary_location":{"id":"doi:10.1109/ipccc66453.2025.11304630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc66453.2025.11304630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Performance, Computing, and Communications Conference (IPCCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101338076","display_name":"Shuaihua Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuaihua Zhao","raw_affiliation_strings":["University of Texas at Arlington,Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington,Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121453784","display_name":"Jian Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Zhou","raw_affiliation_strings":["University of Texas at Arlington,Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington,Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121456312","display_name":"Song Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Jiang","raw_affiliation_strings":["University of Texas at Arlington,Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington,Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189196454"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.26820001006126404,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.26820001006126404,"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/T11106","display_name":"Data Management and Algorithms","score":0.2409999966621399,"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/T11719","display_name":"Data Quality and Management","score":0.17640000581741333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.8561999797821045},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.43290001153945923},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42179998755455017},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.37689998745918274}],"concepts":[{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.8561999797821045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7458999752998352},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4357999861240387},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4043000042438507},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C134835016","wikidata":"https://www.wikidata.org/wiki/Q690265","display_name":"Lookup table","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipccc66453.2025.11304630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc66453.2025.11304630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Performance, Computing, and Communications Conference (IPCCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6700482096","display_name":null,"funder_award_id":"CCF-2313146","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2109906716","https://openalex.org/W2161694911","https://openalex.org/W2201220957","https://openalex.org/W2412642011","https://openalex.org/W2945486614","https://openalex.org/W2948233700","https://openalex.org/W2962771342","https://openalex.org/W2979531022","https://openalex.org/W2999149038","https://openalex.org/W3033065823","https://openalex.org/W3080797093","https://openalex.org/W3096737792","https://openalex.org/W3164254023","https://openalex.org/W4206548428","https://openalex.org/W4284965069","https://openalex.org/W4284974261","https://openalex.org/W4381621971","https://openalex.org/W4399174588","https://openalex.org/W4399208580","https://openalex.org/W4402043003"],"related_works":[],"abstract_inverted_index":{"Learned":[0],"indexes":[1,14,53,64,79,125,241],"have":[2],"received":[3],"significant":[4],"attention":[5],"for":[6,90,223],"their":[7],"potential":[8],"to":[9,29,85,164,187,256,258],"dramatically":[10],"outperform":[11],"traditional":[12,250],"treebased":[13],"in":[15,25,49,59,148],"both":[16],"speed":[17],"and":[18,45,112,229,245],"space":[19],"efficiency.":[20],"Their":[21],"core":[22],"strength":[23],"lies":[24],"using":[26],"predictive":[27],"models":[28,57,84,182,209],"estimate":[30],"the":[31,87,96,188,191,195,216,221],"position":[32],"of":[33,123,190,197],"a":[34,37,60,68,73,91,134,212],"key":[35,43,154,159,171,185,201,218],"within":[36],"sorted":[38],"array.":[39],"To":[40],"handle":[41],"complex":[42],"distributions":[44],"support":[46],"frequent":[47],"insertions":[48],"dynamic":[50],"workloads,":[51],"learned":[52,78,136,240],"typically":[54],"organize":[55],"multiple":[56],"hierarchically":[58],"tree":[61],"structure.":[62],"These":[63,109],"perform":[65],"best":[66],"when":[67],"model":[69,114,192,198],"can":[70,116],"accurately":[71],"predict":[72],"key's":[74],"location.":[75],"However,":[76],"existing":[77],"often":[80],"require":[81],"traversing":[82],"several":[83],"reach":[86],"one":[88],"responsible":[89],"target":[92],"key.":[93],"Moreover,":[94],"if":[95],"prediction":[97,175],"is":[98,107],"imprecise,":[99],"an":[100],"additional":[101],"local":[102],"search":[103],"(the":[104],"last-mile":[105,177],"search)":[106],"needed.":[108],"overheads":[110],"before":[111],"after":[113],"execution":[115],"significantly":[117],"degrade":[118],"performance,":[119],"sometimes":[120],"approaching":[121],"that":[122,139,210,236],"conventional":[124],"like":[126,252],"B+-trees.":[127],"In":[128],"this":[129],"paper,":[130],"we":[131],"propose":[132],"LASER,":[133],"new":[135],"index":[137],"design":[138],"tackles":[140],"these":[141],"inefficiencies":[142],"by":[143,254],"leveraging":[144],"two":[145],"common":[146],"patterns":[147],"real-world":[149],"workloads.":[150],"First,":[151],"LASER":[152,179,228,237],"exploits":[153],"access":[155],"locality,":[156],"where":[157],"some":[158],"ranges":[160,186,202],"are":[161],"more":[162],"likely":[163],"be":[165],"accessed.":[166],"Second,":[167],"it":[168,207],"detects":[169],"linear":[170,205],"distributions,":[172,206],"enabling":[173],"precise":[174],"without":[176],"search.":[178,225],"adaptively":[180],"promotes":[181],"covering":[183],"long":[184],"top":[189],"tree,":[193],"reducing":[194],"number":[196],"traversals.":[199],"For":[200],"with":[203],"perfectly":[204],"employs":[208],"guarantee":[211],"direct":[213],"hit":[214],"(on":[215],"exact":[217],"position),":[219],"eliminating":[220],"need":[222],"further":[224],"We":[226],"implemented":[227],"conducted":[230],"extensive":[231],"evaluations.":[232],"The":[233],"results":[234],"show":[235],"outperforms":[238],"state-of-the-art":[239],"such":[242],"as":[243,247,249],"LIPP":[244],"ALEX,":[246],"well":[248],"indexes,":[251],"ART,":[253],"up":[255],"1.6":[257],"5.5":[259],"times.":[260]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-29T00:00:00"}