{"id":"https://openalex.org/W7138376708","doi":"https://doi.org/10.48550/arxiv.2603.14899","title":"A New Lower Bounding Paradigm and Tighter Lower Bounds for Elastic Similarity Measures","display_name":"A New Lower Bounding Paradigm and Tighter Lower Bounds for Elastic Similarity Measures","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138376708","doi":"https://doi.org/10.48550/arxiv.2603.14899"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14899","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14899","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075597781","display_name":"Zemin Chao","orcid":"https://orcid.org/0000-0003-0784-6650"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chao, Zemin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110975219","display_name":"Boyu Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Boyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129692478","display_name":"Zitong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zitong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122913514","display_name":"Zhixin Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Zhixin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129686880","display_name":"Xianglong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xianglong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129748608","display_name":"Hongzhi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hongzhi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5075597781"],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9592999815940857,"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":0.9592999815940857,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.007400000002235174,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.004000000189989805,"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/bounding-overwatch","display_name":"Bounding overwatch","score":0.7419000267982483},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6312000155448914},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5245000123977661},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5138000249862671},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5056999921798706},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.49709999561309814},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.48410001397132874},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.46470001339912415},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.40849998593330383}],"concepts":[{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7419000267982483},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6312000155448914},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5245000123977661},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.49709999561309814},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45570001006126404},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4530999958515167},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4052000045776367},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31209999322891235},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C121854251","wikidata":"https://www.wikidata.org/wiki/Q62932","display_name":"Elasticity (physics)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C182964748","wikidata":"https://www.wikidata.org/wiki/Q208216","display_name":"Triangle inequality","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14899","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14899","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Elastic":[0],"similarity":[1,8,56,89,188,210,216],"measures":[2,19,90,141],"are":[3,20,117],"fundamental":[4],"to":[5,14,31,50,183],"time":[6,76,208],"series":[7,209],"search":[9,211],"because":[10],"of":[11,28,54,82,87,103,205],"their":[12],"ability":[13],"handle":[15],"temporal":[16],"misalignments.":[17],"These":[18],"inherently":[21],"computationally":[22],"expensive,":[23],"therefore":[24],"necessitating":[25],"the":[26,74,83,104,133,202],"use":[27],"lower":[29,46,61,85,114,136],"bounds":[30,86,115,137],"prune":[32],"unnecessary":[33],"comparisons.":[34],"This":[35,58],"paper":[36],"proposes":[37],"a":[38,51,64,68,157,194],"new":[39],"\\emph{Bipartite":[40],"Graph":[41],"Edge-Cover":[42],"Paradigm}":[43],"for":[44,138,154],"deriving":[45],"bounds,":[47],"which":[48],"applies":[49],"broad":[52],"class":[53],"elastic":[55,88,140,187,215],"measures.":[57,189,217],"paradigm":[59,106],"formulates":[60],"bounding":[62],"as":[63,94],"vertex-weighting":[65],"problem":[66],"on":[67,125,214],"weighted":[69],"bipartite":[70],"graph":[71],"induced":[72],"from":[73,181],"input":[75],"series.":[77],"Under":[78],"this":[79],"paradigm,":[80],"most":[81],"existing":[84],"can":[91],"be":[92],"viewed":[93],"simple":[95],"instantiations.":[96],"We":[97],"further":[98],"propose":[99],"\\textit{BGLB},":[100],"an":[101,109],"instantiation":[102],"proposed":[105],"that":[107,116,130],"incorporates":[108],"additional":[110],"augmentation":[111],"term,":[112],"yielding":[113],"provably":[118],"tighter.":[119],"Theoretical":[120],"analysis":[121],"and":[122,147,162],"extensive":[123],"experiments":[124],"128":[126],"real-world":[127],"datasets":[128],"demonstrate":[129],"\\textit{BGLB}":[131,150,170,191,206],"achieves":[132],"tightest":[134],"known":[135],"six":[139],"(ERP,":[142],"MSM,":[143],"TWED,":[144],"LCSS,":[145],"EDR,":[146],"SWALE).":[148],"Moreover,":[149],"remains":[151],"highly":[152],"competitive":[153],"\\textit{DTW}":[155],"with":[156],"favorable":[158],"trade-off":[159],"between":[160],"tightness":[161],"computational":[163],"efficiency.":[164],"In":[165],"nearest":[166],"neighbor":[167],"search,":[168],"integrating":[169],"into":[171],"filter":[172],"pipelines":[173],"consistently":[174],"outperforms":[175],"state-of-the-art":[176],"methods,":[177],"achieving":[178],"speedups":[179],"ranging":[180],"$24.6\\%$":[182],"$84.9\\%$":[184],"across":[185],"various":[186],"Besides,":[190],"also":[192],"delivers":[193],"significant":[195],"acceleration":[196],"in":[197,207],"density-based":[198],"clustering":[199],"applications,":[200],"validating":[201],"practical":[203],"potential":[204],"tasks":[212],"based":[213]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}