{"id":"https://openalex.org/W2999868990","doi":"https://doi.org/10.1145/3341161.3343518","title":"Multivariate motif detection in local weather big data","display_name":"Multivariate motif detection in local weather big data","publication_year":2019,"publication_date":"2019-08-27","ids":{"openalex":"https://openalex.org/W2999868990","doi":"https://doi.org/10.1145/3341161.3343518","mag":"2999868990"},"language":"en","primary_location":{"id":"doi:10.1145/3341161.3343518","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341161.3343518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","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/A5083041537","display_name":"Konstantinos F. Xylogiannopoulos","orcid":"https://orcid.org/0000-0003-2376-898X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Konstantinos Xylogiannopoulos","raw_affiliation_strings":["University of Calgary, Calgary, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034281379","display_name":"Panagiotis Karampelas","orcid":"https://orcid.org/0000-0003-1684-7612"},"institutions":[{"id":"https://openalex.org/I2802113776","display_name":"Hellenic Air Force","ror":"https://ror.org/044xk2674","country_code":"GR","type":"government","lineage":["https://openalex.org/I2802113776"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Panagiotis Karampelas","raw_affiliation_strings":["Hellenic Air Force Academy, Dekelia, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hellenic Air Force Academy, Dekelia, Greece","institution_ids":["https://openalex.org/I2802113776"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066265052","display_name":"Reda Alhajj","orcid":"https://orcid.org/0000-0001-6657-9738"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Reda Alhajj","raw_affiliation_strings":["University of Calgary, Calgary, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.878,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75601443,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"749","last_page":"756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.984000027179718,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.960099995136261,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-weather","display_name":"Extreme weather","score":0.6234957575798035},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6054794788360596},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.6025778651237488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5618795156478882},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5178387761116028},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.47047311067581177},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.46905091404914856},{"id":"https://openalex.org/keywords/natural-disaster","display_name":"Natural disaster","score":0.4659869074821472},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.4600856304168701},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4496574103832245},{"id":"https://openalex.org/keywords/globe","display_name":"Globe","score":0.43407565355300903},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4337650239467621},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4012024700641632},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.37672188878059387},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34912803769111633},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2690933346748352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21903866529464722}],"concepts":[{"id":"https://openalex.org/C205537798","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Extreme weather","level":3,"score":0.6234957575798035},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6054794788360596},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.6025778651237488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5618795156478882},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5178387761116028},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.47047311067581177},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.46905091404914856},{"id":"https://openalex.org/C166566181","wikidata":"https://www.wikidata.org/wiki/Q8065","display_name":"Natural disaster","level":2,"score":0.4659869074821472},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.4600856304168701},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4496574103832245},{"id":"https://openalex.org/C2775899829","wikidata":"https://www.wikidata.org/wiki/Q3109007","display_name":"Globe","level":2,"score":0.43407565355300903},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4337650239467621},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4012024700641632},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.37672188878059387},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34912803769111633},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2690933346748352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21903866529464722},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341161.3343518","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341161.3343518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W599041235","https://openalex.org/W1550657683","https://openalex.org/W1570448133","https://openalex.org/W1641140809","https://openalex.org/W1673310716","https://openalex.org/W1838634798","https://openalex.org/W1989477967","https://openalex.org/W2002521620","https://openalex.org/W2025274119","https://openalex.org/W2031964839","https://openalex.org/W2043039687","https://openalex.org/W2047952927","https://openalex.org/W2068689590","https://openalex.org/W2087560201","https://openalex.org/W2096239328","https://openalex.org/W2132219376","https://openalex.org/W2164000012","https://openalex.org/W2319265692","https://openalex.org/W2601703135","https://openalex.org/W2739227211","https://openalex.org/W4302352187","https://openalex.org/W6910787570"],"related_works":["https://openalex.org/W2028495302","https://openalex.org/W4396872084","https://openalex.org/W4249498729","https://openalex.org/W4388489128","https://openalex.org/W2002261065","https://openalex.org/W1513656766","https://openalex.org/W1967083444","https://openalex.org/W2484263418","https://openalex.org/W4281661178","https://openalex.org/W3176490725"],"abstract_inverted_index":{"In":[0,152],"recent":[1],"years,":[2],"there":[3,16,55],"are":[4,17],"very":[5],"frequent":[6],"reports":[7,19],"of":[8,39,72,81,89,111,116,138,220],"disasters":[9,32],"attributed":[10],"to":[11,48,100,133,147,167,196,216,235],"the":[12,37,49,70,73,78,82,109,136,139,144,155,200,218,221,247],"climate":[13,50],"change":[14],"and":[15,104,142,227,250],"several":[18],"that":[20,60,164,213,267],"these":[21],"extreme":[22,112],"phenomena":[23,63],"will":[24],"further":[25],"affect":[26],"people":[27],"not":[28,67,97,194],"only":[29,68],"as":[30,43],"weather":[31,62,93,117,131,140,174,190,222,244,261],"but":[33,75],"also":[34,76],"indirectly":[35],"with":[36,199],"shortage":[38],"natural":[40],"resources":[41],"such":[42,85],"water":[44],"or":[45,178],"food":[46],"due":[47],"change.":[51],"Towards":[52],"this":[53,153],"direction,":[54],"is":[56,165,193,207,214,233],"an":[57],"on-going":[58],"research":[59],"studies":[61],"by":[64],"collecting":[65],"data":[66,103,118,125,161,211,223,245],"in":[69,176,181,224,259],"surface":[71],"globe":[74],"at":[77],"different":[79,173,225],"levels":[80],"atmosphere.":[83],"Having":[84],"a":[86,123,159,182,210,228],"large":[87],"volume":[88],"data,":[90],"traditional":[91,201],"numerical":[92,202],"prediction":[94,110,150],"models":[95],"may":[96,129],"be":[98],"able":[99,166,215,234],"assimilate":[101],"those":[102],"extract":[105],"knowledge":[106,145],"useful":[107,187],"for":[108,263],"phenomena.":[113],"Thus,":[114],"analysis":[115],"has":[119],"been":[120],"transformed":[121],"into":[122],"big":[124,160],"analytics":[126,162],"problem":[127],"which":[128,192,232],"enable":[130],"scientists":[132],"better":[134],"understand":[135],"interrelations":[137],"variables":[141,175,191,262],"use":[143],"discovered":[146],"improve":[148],"their":[149],"models.":[151],"context,":[154],"current":[156],"paper":[157],"proposes":[158],"methodology":[163,206],"detect":[168,197,236],"all":[169,237],"common":[170,238],"patterns":[171,258],"between":[172,189],"neighboring":[177],"distant":[179],"points":[180],"specific":[183,265],"time":[184],"window":[185],"revealing":[186],"associations":[188],"possible":[195],"otherwise":[198,256],"methods.":[203],"The":[204,240],"proposed":[205],"based":[208],"on":[209],"structure":[212],"store":[217],"magnitude":[219],"dimensions":[226],"pattern":[229],"detection":[230],"algorithm":[231],"patterns.":[239],"experimental":[241],"results":[242],"using":[243],"from":[246],"National":[248],"Oceanic":[249],"Atmospheric":[251],"Administration":[252],"(NOAA)":[253],"revealed":[254],"interesting":[255],"unknown":[257],"two":[260,264],"locations":[266],"were":[268],"studied.":[269]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
