{"id":"https://openalex.org/W6889806632","doi":"https://doi.org/10.26190/unsworks/30897","title":"Efficient kNN Join over Dynamic High-dimensional Data","display_name":"Efficient kNN Join over Dynamic High-dimensional Data","publication_year":2025,"publication_date":"2025-03-03","ids":{"openalex":"https://openalex.org/W6889806632","doi":"https://doi.org/10.26190/unsworks/30897"},"language":"en","primary_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/104204","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/104204","pdf_url":"https://unsworks.unsw.edu.au/bitstreams/b7e9d5df-7997-4bc2-8aae-c6c22a280a6c/download","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://unsworks.unsw.edu.au/bitstreams/b7e9d5df-7997-4bc2-8aae-c6c22a280a6c/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ukey, Nimish","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ukey, Nimish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.11349999904632568,"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":0.11349999904632568,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.05779999867081642,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.03909999877214432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6575999855995178},{"id":"https://openalex.org/keywords/join","display_name":"Join (topology)","score":0.642799973487854},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5241000056266785},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5070000290870667},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.484499990940094},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.451200008392334},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4180000126361847},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.40130001306533813},{"id":"https://openalex.org/keywords/precomputation","display_name":"Precomputation","score":0.3986000120639801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8331000208854675},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7193999886512756},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6575999855995178},{"id":"https://openalex.org/C2776124973","wikidata":"https://www.wikidata.org/wiki/Q3183033","display_name":"Join (topology)","level":2,"score":0.642799973487854},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5241000056266785},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.451200008392334},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C159379195","wikidata":"https://www.wikidata.org/wiki/Q7239568","display_name":"Precomputation","level":3,"score":0.3986000120639801},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C98199447","wikidata":"https://www.wikidata.org/wiki/Q2445044","display_name":"Materialized view","level":4,"score":0.30399999022483826},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29679998755455017},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.2581999897956848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2531999945640564}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/104204","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/104204","pdf_url":"https://unsworks.unsw.edu.au/bitstreams/b7e9d5df-7997-4bc2-8aae-c6c22a280a6c/download","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},{"id":"doi:10.26190/unsworks/30897","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/30897","pdf_url":null,"source":{"id":"https://openalex.org/S7407053176","display_name":"University of New South Wales","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"}],"best_oa_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/104204","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/104204","pdf_url":"https://unsworks.unsw.edu.au/bitstreams/b7e9d5df-7997-4bc2-8aae-c6c22a280a6c/download","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320965","display_name":"University of New South Wales","ror":"https://ror.org/03r8z3t63"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6889806632.pdf","grobid_xml":"https://content.openalex.org/works/W6889806632.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,406,438],"k":[1,9],"nearest":[2,10],"neighbor":[3],"(kNN)":[4],"join":[5,99,150,272,418],"searches":[6],"for":[7,12,119,193,247,263,301,314,334,395,402],"the":[8,17,22,26,74,81,95,125,144,180,188,201,207,216,238,266,335,344,356,373,384,413,445],"neighbors":[11],"each":[13],"query":[14,18],"point":[15],"in":[16,25,35,83,237,277,296,364,415,462],"dataset":[19,28,202],"U":[20],"from":[21],"answer":[23,27],"points":[24],"I.":[29],"It":[30],"is":[31,55],"a":[32,133,234,278,297,329],"fundamental":[33],"operation":[34],"many":[36,88],"applications,":[37],"including":[38],"databases,":[39],"data":[40,54,84,276,350,466],"mining,":[41],"computer":[42],"vision,":[43],"machine":[44],"learning,":[45],"recommendation":[46],"systems,":[47],"and":[48,86,103,139,146,175,203,224,256,375,426,428,434,447],"more.":[49],"In":[50],"today's":[51],"digital":[52],"era,":[53],"generated":[56],"at":[57],"an":[58,116,166,190,243,284],"astonishing":[59],"rate.":[60],"These":[61],"real-world":[62,460],"high-dimensional":[63,105,121,153,275,404],"datasets":[64,197,443],"are":[65],"dynamic,":[66],"necessitating":[67],"frequent":[68],"updates":[69,293,307,363],"to":[70,93,114,142,319,431,456],"keep":[71],"up":[72],"with":[73,323,388],"constant":[75],"influx":[76],"of":[77,97,127,136,148,182,218,269,305,378,409,449,464],"new":[78,235],"information.":[79],"With":[80],"surge":[82],"volume":[85],"dimensionality,":[87],"approaches":[89],"have":[90],"been":[91],"proposed":[92,451],"enhance":[94,143],"efficiency":[96,145,322,374,425,448],"kNN":[98,149,226,249,271,337,417],"operations":[100,151],"on":[101],"static":[102],"dynamic":[104,120,248,416],"data.":[106,154,213,405],"However,":[107],"we":[108,164,186,241,282,327],"observed":[109],"that":[110,169,290,352,422],"existing":[111,128,183],"research":[112,411],"fails":[113],"offer":[115],"efficient":[117,244,285],"solution":[118,246],"datasets.":[122,437],"To":[123],"address":[124],"limitations":[126],"work,":[129],"this":[130,309],"thesis":[131],"proposes":[132],"comprehensive":[134],"set":[135],"novel":[137],"methods":[138],"optimisations":[140],"designed":[141],"scalability":[147],"over":[152,274],"Our":[155],"contributions":[156],"can":[157],"be":[158],"divided":[159],"into":[160,294],"four":[161],"parts:":[162],"First,":[163],"introduce":[165,328],"HDR+":[167],"Tree":[168],"features":[170],"lazy":[171],"updates,":[172,342,357],"batch":[173,287,302,379],"operations,":[174,419],"optimised":[176],"deletions,":[177,230],"significantly":[178,320,371,457],"improving":[179],"performance":[181,377],"algorithms.":[184],"Second,":[185],"propose":[187,283],"HDR-Forest,":[189],"innovative":[191],"approach":[192,382],"handling":[194,306],"non-globally":[195],"correlated":[196],"efficiently.":[198],"By":[199,361],"clustering":[200],"constructing":[204],"multiple":[205],"HDR-Trees,":[206],"HDR-Forest":[208],"captures":[209],"local":[210],"correlations":[211],"among":[212],"Enhanced":[214],"by":[215],"precomputation":[217],"principal":[219],"component":[220],"analysis":[221,467],"(PCA)":[222],"states":[223],"pruning-based":[225],"recomputation":[227],"during":[228,341],"item":[229],"our":[231,369,410,450],"method":[232,310,333,370],"sets":[233],"benchmark":[236],"field.":[239],"Third,":[240],"pioneer":[242],"parallel":[245],"join,":[250],"leveraging":[251],"Simultaneous":[252],"Multi-Threading":[253],"(SMT)":[254],"technologies":[255],"Single":[257],"Instruction":[258],"Multiple":[259],"Data":[260],"(SIMD)":[261],"instructions":[262],"parallelisation,":[264],"marking":[265],"first":[267],"instance":[268],"exact":[270],"computation":[273,313,385],"parallelised":[279],"manner.":[280],"Lastly,":[281],"cluster-based":[286,330],"update":[288],"technique":[289,347],"groups":[291],"similar":[292,316],"clusters":[295,351,365],"reduced":[298],"dimensional":[299],"space":[300],"processing.":[303],"Instead":[304],"individually,":[308,368],"enables":[311],"shared":[312],"updating":[315,436],"items,":[317],"leading":[318],"improved":[321],"minimal":[324],"overhead.":[325],"Additionally,":[326],"pruned":[331],"search":[332,339],"reversed":[336],"(RkNN)":[338],"process":[340],"utilising":[343],"HDR-Tree.":[345],"This":[346,381],"efficiently":[348],"prunes":[349],"do":[353],"not":[354],"affect":[355],"avoiding":[358],"unnecessary":[359],"computations.":[360],"processing":[362],"rather":[366],"than":[367],"enhances":[372],"overall":[376],"updates.":[380],"reduces":[383],"cost":[386],"associated":[387],"non-leaf":[389],"nodes":[390],"while":[391],"maintaining":[392],"comparable":[393],"costs":[394],"leaf":[396],"nodes,":[397],"making":[398],"it":[399],"highly":[400],"effective":[401],"large-scale":[403],"collective":[407],"outcome":[408],"advances":[412],"state-of-the-art":[414],"offering":[420],"methodologies":[421],"improve":[423],"computational":[424],"scalability,":[427],"extend":[429],"applicability":[430],"more":[432],"diverse":[433],"dynamically":[435],"experimental":[439],"results":[440],"across":[441],"different":[442],"confirm":[444],"effectiveness":[446],"solutions,":[452],"showcasing":[453],"their":[454],"potential":[455],"benefit":[458],"various":[459],"applications":[461],"need":[463],"high-performance":[465],"capabilities.":[468]},"counts_by_year":[],"updated_date":"2026-07-13T05:41:17.278356","created_date":"2025-10-10T00:00:00"}