{"id":"https://openalex.org/W4406460842","doi":"https://doi.org/10.1109/bigdata62323.2024.10825151","title":"Efficient Point-to-Subspace ANNS in Manhattan and L<sub>p</sub> Space by LSH Pruning","display_name":"Efficient Point-to-Subspace ANNS in Manhattan and L<sub>p</sub> Space by LSH Pruning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406460842","doi":"https://doi.org/10.1109/bigdata62323.2024.10825151"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825151","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825151","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5013337954","display_name":"Jingfan Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingfan Meng","raw_affiliation_strings":["Georgia Institute of Technology,Atlanta,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology,Atlanta,GA,USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012792241","display_name":"Huayi Wang","orcid":"https://orcid.org/0000-0001-6274-3844"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huayi Wang","raw_affiliation_strings":["Georgia Institute of Technology,Atlanta,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology,Atlanta,GA,USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025728584","display_name":"Jun Xu","orcid":"https://orcid.org/0000-0002-0046-8119"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Xu","raw_affiliation_strings":["Georgia Institute of Technology,Atlanta,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology,Atlanta,GA,USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39036885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"610","last_page":"619"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.8017020225524902},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7498189210891724},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5857892632484436},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5447924137115479},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.522158145904541},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4561769664287567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3397901654243469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25513267517089844},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.13691610097885132}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.8017020225524902},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7498189210891724},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5857892632484436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5447924137115479},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.522158145904541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4561769664287567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3397901654243469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25513267517089844},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.13691610097885132},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825151","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825151","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":24,"referenced_works":["https://openalex.org/W642889137","https://openalex.org/W1522301498","https://openalex.org/W1526394777","https://openalex.org/W1913628733","https://openalex.org/W1969281479","https://openalex.org/W1993962865","https://openalex.org/W2008995227","https://openalex.org/W2079211793","https://openalex.org/W2093347109","https://openalex.org/W2093813380","https://openalex.org/W2156855109","https://openalex.org/W2157575532","https://openalex.org/W2162006472","https://openalex.org/W2294518132","https://openalex.org/W2911430788","https://openalex.org/W2963265099","https://openalex.org/W3021621143","https://openalex.org/W3102948379","https://openalex.org/W3175564083","https://openalex.org/W4245386093","https://openalex.org/W4317641521","https://openalex.org/W4321448319","https://openalex.org/W6631190155","https://openalex.org/W6771838691"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W1861706286","https://openalex.org/W2219338811","https://openalex.org/W2149583853","https://openalex.org/W2143002539","https://openalex.org/W4293472652","https://openalex.org/W3198856780","https://openalex.org/W597159773"],"abstract_inverted_index":{"Point-to-subspace":[0],"approximate":[1],"nearest":[2],"neighbor":[3],"search":[4],"in":[5,38,65,110,119],"Lpmetric":[6],"(Lp-P2S-ANNS)":[7],"is":[8,19,35,48,62,68,77],"a":[9,56,89,106,126],"challenging":[10],"research":[11],"problem:":[12],"Its":[13],"only":[14],"existing":[15,100],"solution,":[16],"called":[17],"LDL1,":[18],"barely":[20],"faster":[21],"than":[22],"the":[23,122],"na\u00efve":[24],"linear":[25,43],"scan,":[26],"because":[27],"its":[28],"pruning":[29,60,82],"(for":[30],"promising":[31],"ANNS":[32],"candidates)":[33],"metric":[34,61],"P2S":[36,63],"distance":[37,64],"Lp,":[39],"whose":[40,59],"computation":[41],"involves":[42],"or":[44],"convex":[45],"programming":[46],"that":[47,116],"computationally":[49,69],"intensive.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"propose":[55,88,105],"novel":[57],"scheme":[58],"L2instead,":[66],"which":[67,95],"cheaper":[70],"by":[71,125],"four":[72],"orders":[73],"of":[74,128],"magnitude,":[75],"yet":[76],"almost":[78],"as":[79,83],"effective":[80],"for":[81],"LDL1\u2019s":[84],"empirically.":[85],"We":[86],"also":[87],"new":[90],"framework":[91],"named":[92],"LSH":[93],"pruning,":[94],"subsumes":[96],"and":[97,104],"improves":[98],"all":[99],"dimension":[101],"reduction":[102],"schemes,":[103],"performance":[107],"model":[108],"well-grounded":[109],"statistics":[111],"theory.":[112],"Our":[113],"experiments":[114],"show":[115],"these":[117],"contributions":[118],"combination":[120],"reduce":[121],"query":[123],"time":[124],"factor":[127],"4.8":[129],"to":[130],"54.":[131]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
