{"id":"https://openalex.org/W1998451966","doi":"https://doi.org/10.1145/1341012.1341075","title":"A parallel multi-scale region outlier mining algorithm for meteorological data","display_name":"A parallel multi-scale region outlier mining algorithm for meteorological data","publication_year":2007,"publication_date":"2007-11-07","ids":{"openalex":"https://openalex.org/W1998451966","doi":"https://doi.org/10.1145/1341012.1341075","mag":"1998451966"},"language":"en","primary_location":{"id":"doi:10.1145/1341012.1341075","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1341012.1341075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems","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/A5046105539","display_name":"Sajib Barua","orcid":"https://orcid.org/0000-0002-3383-8577"},"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":"Sajib Barua","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":"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":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168635309"],"apc_list":null,"apc_paid":null,"fwci":0.8903,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72524866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.987500011920929,"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/computer-science","display_name":"Computer science","score":0.7310606241226196},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7063140869140625},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.703916072845459},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6984630227088928},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6790196895599365},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5529862642288208},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5151821374893188},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4524383544921875},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.44688552618026733},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22738322615623474},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.21295616030693054},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10968208312988281},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10316762328147888}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7310606241226196},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7063140869140625},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.703916072845459},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6984630227088928},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6790196895599365},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5529862642288208},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5151821374893188},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4524383544921875},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.44688552618026733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22738322615623474},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.21295616030693054},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10968208312988281},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10316762328147888},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1341012.1341075","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1341012.1341075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W92454686","https://openalex.org/W1543661672","https://openalex.org/W1591711043","https://openalex.org/W1970019709","https://openalex.org/W2057117782","https://openalex.org/W2062024414","https://openalex.org/W2070983531","https://openalex.org/W2096793442","https://openalex.org/W2132984323"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W2371336807"],"abstract_inverted_index":{"The":[0],"increase":[1],"use":[2],"of":[3,64,69,86],"high":[4],"dimensional,":[5],"geographically":[6],"distributed":[7],"rich":[8],"and":[9,84,124],"massive":[10],"meteorological":[11,25,70,128],"data":[12,26,71,76],"poses":[13],"an":[14,51],"increasing":[15],"scientific":[16],"challenge":[17,68],"in":[18,23,31],"efficient":[19],"outlier":[20,55,104],"mining.":[21],"Properties":[22],"such":[24],"are":[27],"observed":[28],"to":[29,80,96],"fluctuate":[30],"spatial":[32,36,40,91,113],"synchrony.":[33],"Capturing":[34],"this":[35,47],"variation":[37],"at":[38,57],"different":[39,58,82],"scales":[41,59],"requires":[42],"a":[43,98],"multi-resolution":[44,62],"analysis.":[45,66],"In":[46],"paper,":[48],"we":[49],"develop":[50],"algorithm":[52,102,116],"for":[53,103,111],"region":[54],"detection":[56,105],"using":[60],"the":[61,75,90],"feature":[63],"wavelet":[65],"Another":[67],"mining":[72],"is":[73,78],"that":[74],"size":[77],"huge":[79],"accommodate":[81],"resolutions":[83],"number":[85],"samples":[87],"varies":[88],"with":[89],"scales.":[92,114],"This":[93],"motivated":[94],"us":[95],"design":[97],"load":[99],"adaptive":[100],"parallel":[101],"which":[106],"can":[107],"maintain":[108],"good":[109],"scalability":[110],"all":[112],"Our":[115],"has":[117],"been":[118],"implemented":[119],"on":[120,126],"high-performance":[121],"computing":[122],"architecture":[123],"evaluated":[125],"real-world":[127],"data.":[129]},"counts_by_year":[{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}