{"id":"https://openalex.org/W4390187819","doi":"https://doi.org/10.1109/qrs60937.2023.00011","title":"Online Data Drift Detection for Anomaly Detection Services based on Deep Learning towards Multivariate Time Series","display_name":"Online Data Drift Detection for Anomaly Detection Services based on Deep Learning towards Multivariate Time Series","publication_year":2023,"publication_date":"2023-10-22","ids":{"openalex":"https://openalex.org/W4390187819","doi":"https://doi.org/10.1109/qrs60937.2023.00011"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs60937.2023.00011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","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/A5103100971","display_name":"G. Tan","orcid":"https://orcid.org/0009-0008-6580-1470"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"funder","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gou Tan","raw_affiliation_strings":["School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335060","display_name":"Pengfei Chen","orcid":"https://orcid.org/0000-0003-0972-6900"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"funder","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Chen","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100400789","display_name":"Min Li","orcid":"https://orcid.org/0009-0001-2289-1815"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"funder","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Li","raw_affiliation_strings":["School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.271,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.499378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":65,"max":76},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9999,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9983,"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/interpretability","display_name":"Interpretability","score":0.7988024},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7305281},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept Drift","score":0.5365479},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.48983905}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8383551},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7988024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.741158},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7305281},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.62740827},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.61884725},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6070377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6031722},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.59693825},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5435067},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5365479},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.48983905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44497526},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4287619},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.11063346},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.080925316},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/qrs60937.2023.00011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320337504","funder_display_name":"Research and Development","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":50,"referenced_works":["https://openalex.org/W1556344493","https://openalex.org/W1965395441","https://openalex.org/W2038819732","https://openalex.org/W2099419573","https://openalex.org/W2157103390","https://openalex.org/W2244109919","https://openalex.org/W2289463038","https://openalex.org/W2407991977","https://openalex.org/W2509304078","https://openalex.org/W2585528949","https://openalex.org/W2620661538","https://openalex.org/W2766761849","https://openalex.org/W2889249015","https://openalex.org/W2898017895","https://openalex.org/W2906498146","https://openalex.org/W2909877301","https://openalex.org/W2911200746","https://openalex.org/W2946595616","https://openalex.org/W2950361482","https://openalex.org/W2962736999","https://openalex.org/W2963274201","https://openalex.org/W3014106621","https://openalex.org/W3044058243","https://openalex.org/W3081497074","https://openalex.org/W3106543020","https://openalex.org/W3108206468","https://openalex.org/W3111082901","https://openalex.org/W3120331202","https://openalex.org/W3134670202","https://openalex.org/W3135644052","https://openalex.org/W3158109590","https://openalex.org/W3166319166","https://openalex.org/W3167971312","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3176476506","https://openalex.org/W3198381997","https://openalex.org/W3216644311","https://openalex.org/W4214942037","https://openalex.org/W4230715394","https://openalex.org/W4281384434","https://openalex.org/W4287825762","https://openalex.org/W4291034555","https://openalex.org/W4293718132","https://openalex.org/W4306645406","https://openalex.org/W4308827956","https://openalex.org/W4312732203","https://openalex.org/W4324066119","https://openalex.org/W4379538689","https://openalex.org/W4383898408"],"related_works":["https://openalex.org/W4399531511","https://openalex.org/W4390547933","https://openalex.org/W4364322549","https://openalex.org/W4363671829","https://openalex.org/W4200247875","https://openalex.org/W3194885736","https://openalex.org/W3186512740","https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W2066625485"],"abstract_inverted_index":{"Deep":[0],"learning":[1,96,104],"models":[2,19,38,105,206],"have":[3],"been":[4],"successfully":[5],"adopted":[6],"in":[7,15,46,55,106,195,215],"anomaly":[8,113,185,204],"detection":[9,89,186,194,205],"for":[10,155],"multivariate":[11,110,196],"time":[12,25,32,111,137,197],"series":[13,33,112,138],"data":[14,87,123,139,143,209],"various":[16,178],"fields.":[17],"These":[18],"are":[20,151],"good":[21],"at":[22],"capturing":[23],"complex":[24],"dependencies":[26],"and":[27,73,81,125,131,140,164,181,199],"extracting":[28],"meaningful":[29],"patterns":[30],"from":[31,145,177],"data.":[34],"However,":[35],"the":[36,56,68,71,107,146,159,190,203,228,231],"trained":[37],"may":[39],"become":[40],"outdated":[41],"due":[42],"to":[43,52,65,77,101],"unforeseen":[44],"changes":[45],"real-world":[47,175],"data,":[48],"which":[49],"can":[50,161],"lead":[51],"a":[53],"decrease":[54],"quality":[57],"of":[58,70,109,118,192,227,230],"model":[59,72,141,147,232],"service.":[60],"Therefore,":[61],"it":[62],"is":[63,168,218],"crucial":[64],"continuously":[66],"monitor":[67,102],"performance":[69],"analyze":[74],"its":[75,79],"behavior":[76],"ensure":[78],"reliability":[80],"availability.":[82],"We":[83,134,188],"propose":[84],"an":[85,93,225],"online":[86],"drift":[88,129,132,156,167,193,210],"method":[90,116,160],"that":[91],"uses":[92],"unsupervised":[94],"deep":[95,103],"network,":[97],"Variational":[98],"Autoencoder":[99],"(VAE),":[100],"field":[108],"detection.":[114,157,211],"This":[115],"consists":[117],"three":[119,174],"main":[120],"steps":[121],"namely":[122],"collection":[124],"statistical":[126],"analysis,":[127],"real-time":[128],"detection,":[130],"interpretation.":[133],"collect":[135],"raw":[136],"prediction":[142],"non-invasively":[144],"server.":[148],"Then":[149],"they":[150],"separated":[152],"into":[153],"windows":[154],"Furthermore,":[158],"provide":[162,224],"analysis":[163,226],"interpretation":[165],"when":[166],"detected.":[169],"Our":[170],"evaluation":[171],"experiments":[172],"involve":[173],"datasets":[176],"industrial":[179],"domains":[180],"four":[182],"different":[183],"structured":[184],"models.":[187],"validate":[189],"effectiveness":[191],"series,":[198],"then":[200],"test":[201],"how":[202],"perform":[207],"during":[208],"The":[212],"highest":[213],"improvement":[214],"F1":[216],"score":[217],"approximately":[219],"0.16.":[220],"In":[221],"addition,":[222],"we":[223],"interpretability":[229],"performance.":[233]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4390187819","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-04-22T06:21:02.580725","created_date":"2023-12-26"}