{"id":"https://openalex.org/W4416036254","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.805","title":"Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases","display_name":"Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416036254","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.805"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.805","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.805","pdf_url":"https://aclanthology.org/2025.emnlp-main.805.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-main.805.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120056869","display_name":"Harshil Vejendla","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]},{"id":"https://openalex.org/I4210096112","display_name":"Rutgers Sexual and Reproductive Health and Rights","ror":"https://ror.org/00rcvgx40","country_code":"NL","type":"other","lineage":["https://openalex.org/I4210096112"]},{"id":"https://openalex.org/I4210123151","display_name":"R\u00fctgers (Germany)","ror":"https://ror.org/02wmkbh90","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210123151"]}],"countries":["DE","NL","US"],"is_corresponding":true,"raw_author_name":"Harshil Vejendla","raw_affiliation_strings":["Rutgers University -New Brunswick"],"affiliations":[{"raw_affiliation_string":"Rutgers University -New Brunswick","institution_ids":["https://openalex.org/I4210123151","https://openalex.org/I102322142","https://openalex.org/I4210096112"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5120056869"],"corresponding_institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4210096112","https://openalex.org/I4210123151"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40218182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"15949","last_page":"15960"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.49000000953674316,"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"}},"topics":[{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.49000000953674316,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.10220000147819519,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.08579999953508377,"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/embedding","display_name":"Embedding","score":0.5095000267028809},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.32100000977516174},{"id":"https://openalex.org/keywords/data-model","display_name":"Data model (GIS)","score":0.3075999915599823},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.27810001373291016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649399995803833},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5095000267028809},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.4893999993801117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38839998841285706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36070001125335693},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C100463513","wikidata":"https://www.wikidata.org/wiki/Q5227322","display_name":"Data model (GIS)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2393999993801117}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.805","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.805","pdf_url":"https://aclanthology.org/2025.emnlp-main.805.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.805","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.805","pdf_url":"https://aclanthology.org/2025.emnlp-main.805.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416036254.pdf","grobid_xml":"https://content.openalex.org/works/W4416036254.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Upgrading":[0],"embedding":[1,40,51],"models":[2],"in":[3],"production":[4],"vector":[5],"databases":[6],"typically":[7],"necessitates":[8],"reencoding":[9],"the":[10,15,49,55,59,108,162],"entire":[11],"corpus":[12],"and":[13,26,76,94,141,161],"rebuilding":[14],"Approximate":[16],"Nearest":[17],"Neighbor":[18],"(ANN)":[19],"index,":[20,62],"leading":[21],"to":[22,38,124,150,158],"significant":[23],"operational":[24,125,146],"disruption":[25],"computational":[27],"cost.This":[28],"paper":[29],"presents":[30],"Drift-Adapter,":[31],"a":[32,77,83,95,114,174],"lightweight,":[33],"learnable":[34],"transformation":[35],"layer":[36],"designed":[37],"bridge":[39],"spaces":[41],"between":[42],"model":[43,98,152,179],"versions.By":[44],"mapping":[45],"new":[46],"queries":[47],"into":[48],"legacy":[50],"space,":[52],"Drift-Adapter":[53,104,133],"enables":[54],"continued":[56],"use":[57],"of":[58,86,107,113,164],"existing":[60],"ANN":[61],"effectively":[63],"deferring":[64],"full":[65,115,128],"re-computation.We":[66],"systematically":[67],"evaluate":[68],"three":[69],"adapter":[70],"parameterizations:":[71],"Orthogonal":[72],"Procrustes,":[73],"Low-Rank":[74],"Affine,":[75],"compact":[78],"Residual":[79],"MLP,":[80],"trained":[81],"on":[82,90],"small":[84],"sample":[85],"paired":[87],"old/new":[88],"embeddings.Experiments":[89],"MTEB":[91],"text":[92],"corpora":[93],"CLIP":[96],"image":[97],"upgrade":[99],"(1M":[100],"items)":[101],"show":[102],"that":[103],"recovers":[105],"95-99%":[106],"retrieval":[109],"recall":[110],"(Recall@10,":[111],"MRR)":[112],"re-embedding,":[116],"adding":[117],"less":[118],"than":[119],"10":[120],"s":[121],"query":[122],"latency.Compared":[123],"strategies":[126],"like":[127,167],"re-indexing":[129],"or":[130],"dual-index":[131],"serving,":[132],"dramatically":[134],"reduces":[135],"recompute":[136],"costs":[137],"(by":[138],"over":[139],"100)":[140],"facilitates":[142],"upgrades":[143],"with":[144],"near-zero":[145],"interruption.We":[147],"analyze":[148],"robustness":[149],"varied":[151],"drift,":[153],"training":[154],"data":[155],"size,":[156],"scalability":[157],"billion-item":[159],"systems,":[160],"impact":[163],"design":[165],"choices":[166],"diagonal":[168],"scaling,":[169],"demonstrating":[170],"Drift-Adapter's":[171],"viability":[172],"as":[173],"pragmatic":[175],"solution":[176],"for":[177],"agile":[178],"deployment.":[180]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2025-11-08T00:00:00"}
