{"id":"https://openalex.org/W4413360772","doi":"https://doi.org/10.1109/icde65448.2025.00303","title":"Compatible Unsupervised Anomaly Detection with Multi-Perspective Spatio-Temporal Learning","display_name":"Compatible Unsupervised Anomaly Detection with Multi-Perspective Spatio-Temporal Learning","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413360772","doi":"https://doi.org/10.1109/icde65448.2025.00303"},"language":"en","primary_location":{"id":"doi:10.1109/icde65448.2025.00303","doi":"https://doi.org/10.1109/icde65448.2025.00303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00303","pdf_url":null,"source":{"id":"https://openalex.org/S4210210321","display_name":"IEEE International Conference on Data Engineering (ICDE)","issn_l":"1084-4627","issn":["1084-4627","2375-0286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5033903725","display_name":"Tingyang Chen","orcid":"https://orcid.org/0009-0008-5635-9326","observed_orcids":["https://orcid.org/0009-0008-5635-9326"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingyang Chen","raw_affiliation_strings":["Huazhong University of Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031485610","display_name":"Bolong Zheng","orcid":"https://orcid.org/0000-0001-8639-4570","observed_orcids":["https://orcid.org/0000-0001-8639-4570"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bolong Zheng","raw_affiliation_strings":["Huazhong University of Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066308099","display_name":"Shuncheng Liu","orcid":"https://orcid.org/0000-0003-3557-6598","observed_orcids":["https://orcid.org/0000-0003-3557-6598"]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuncheng Liu","raw_affiliation_strings":["Huawei,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109757703","display_name":"Zhuorong Fan","orcid":"https://orcid.org/0009-0007-2832-9595","observed_orcids":["https://orcid.org/0009-0007-2832-9595"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhujiong Fan","raw_affiliation_strings":["Huazhong University of Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100983740","display_name":"Zhi Xiang Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Xu","raw_affiliation_strings":["Huawei,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113062538","display_name":"Lingsen Yan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingsen Yan","raw_affiliation_strings":["Huazhong University of Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032503782","display_name":"Kai Zeng","orcid":"https://orcid.org/0009-0005-5788-5668","observed_orcids":["https://orcid.org/0009-0005-5788-5668"]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Zeng","raw_affiliation_strings":["Huawei,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tao Ye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Ye","raw_affiliation_strings":["Huawei,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051566631","display_name":"Xiaofang Zhou","orcid":"https://orcid.org/0000-0001-6343-1455","observed_orcids":["https://orcid.org/0000-0001-6343-1455"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaofang Zhou","raw_affiliation_strings":["Hong Kong University of Science and Technology,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology,Hong Kong,China","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.368,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62306617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4066","last_page":"4078"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9839000105857849,"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":0.9839000105857849,"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/T12127","display_name":"Software System Performance and Reliability","score":0.013899999670684338,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/unsupervised-anomaly-detection","display_name":"unsupervised anomaly detection","score":0.8989999890327454},{"id":"https://openalex.org/keywords/spatiotemporal-learning","display_name":"spatiotemporal learning","score":0.8640000224113464},{"id":"https://openalex.org/keywords/multivariate-time-series","display_name":"multivariate time series","score":0.8880000114440918},{"id":"https://openalex.org/keywords/distributed-cloud-systems","display_name":"distributed cloud systems","score":0.8989999890327454},{"id":"https://openalex.org/keywords/industrial-maintenance","display_name":"industrial maintenance","score":0.6489999890327454},{"id":"https://openalex.org/keywords/autoencoder-reconstruction","display_name":"autoencoder reconstruction","score":0.6729999780654907},{"id":"https://openalex.org/keywords/adversarial-training","display_name":"adversarial training","score":0.7739999890327454},{"id":"https://openalex.org/keywords/min-max-optimization","display_name":"minimax optimization","score":0.7879999876022339},{"id":"https://openalex.org/keywords/pseudo-labeling","display_name":"pseudo-labeling","score":0.6039999723434448},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.7139999866485596},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.6909999847412109},{"id":"https://openalex.org/keywords/stamps","display_name":"stamps","score":0.6949999928474426}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7780052423477173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7012227773666382},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6497797966003418},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5721070170402527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5093163251876831},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.44936975836753845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3285391330718994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32555803656578064},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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":2,"locations":[{"id":"doi:10.1109/icde65448.2025.00303","doi":"https://doi.org/10.1109/icde65448.2025.00303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00303","pdf_url":null,"source":{"id":"https://openalex.org/S4210210321","display_name":"IEEE International Conference on Data Engineering (ICDE)","issn_l":"1084-4627","issn":["1084-4627","2375-0286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-151619","doi":null,"is_oa":false,"landing_page_url":"http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2025&rft.spage=&rft.aulast=Chen&rft.aufirst=Tingyang&rft.atitle=Compatible+Unsupervised+Anomaly+Detection+with+Multi-Perspective+Spatio-Temporal+Learning&rft.title=","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4943999946117401,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G8512540673","display_name":null,"funder_award_id":"62372194","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1242748811","https://openalex.org/W1530232915","https://openalex.org/W1986332411","https://openalex.org/W2019014808","https://openalex.org/W2129281431","https://openalex.org/W2137130182","https://openalex.org/W2144182447","https://openalex.org/W2296719434","https://openalex.org/W2626778328","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2833324965","https://openalex.org/W2902758299","https://openalex.org/W2950361482","https://openalex.org/W2965341826","https://openalex.org/W3080253043","https://openalex.org/W3081497074","https://openalex.org/W3087668759","https://openalex.org/W3126367810","https://openalex.org/W3128634608","https://openalex.org/W3151000187","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3176085787","https://openalex.org/W4226060502","https://openalex.org/W4283318673","https://openalex.org/W4283810514","https://openalex.org/W4285600291","https://openalex.org/W4288057688","https://openalex.org/W4289866548","https://openalex.org/W4290943650","https://openalex.org/W4367016885","https://openalex.org/W4385270260","https://openalex.org/W4400229121","https://openalex.org/W4400909504"],"related_works":["https://openalex.org/W7124133843","https://openalex.org/W7123396429","https://openalex.org/W7096827325","https://openalex.org/W4407323899","https://openalex.org/W3162134530","https://openalex.org/W4408355024","https://openalex.org/W4406982854","https://openalex.org/W4411348698","https://openalex.org/W7167012866","https://openalex.org/W7160251924"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,25,165,243],"is":[2,27,104,173],"one":[3],"of":[4,22,39,73,184],"the":[5,20,33,47,111,122,146,188,222,242],"most":[6],"significant":[7],"tasks":[8],"in":[9,15,30,55,217,249],"industrial":[10],"automatic":[11],"maintenance,":[12],"such":[13],"as":[14],"distributed":[16,56,135],"cloud":[17],"systems.":[18,57],"However,":[19],"implementation":[21],"existing":[23],"anomaly":[24],"methods":[26,178],"still":[28],"challenging":[29],"(i)":[31],"capturing":[32],"complex":[34],"spatial":[35],"and":[36,50,82,100,110,192,225,237],"temporal":[37],"correlations":[38],"multivariate":[40],"time":[41],"series,":[42],"(ii)":[43],"effectively":[44,132],"adapting":[45],"to":[46,93,106,120,134,143,179],"unsupervised":[48,154,238],"condition,":[49],"(iii)":[51],"generalizing":[52],"across":[53],"nodes":[54],"To":[58],"address":[59],"these":[60],"challenges,":[61],"we":[62,151],"design":[63],"a":[64,74,78,116,138,170,181,212],"multi-perspective":[65],"spatio-temporal":[66,95],"attention":[67,91],"model,":[68],"called":[69],"STAMP,":[70],"which":[71],"consists":[72],"prediction":[75],"module":[76,80,114],"ST-ATTN,":[77],"reconstruction":[79],"AutoEncoder,":[81],"an":[83,153],"adversarial":[84,112],"optimizing":[85],"module.":[86],"Specifically,":[87],"ST-ATTN":[88],"leverages":[89],"multiple":[90],"mechanisms":[92],"perform":[94],"learning":[96,123],"from":[97],"both":[98,235],"local":[99],"global":[101],"perspectives,":[102],"AutoEncoder":[103],"utilized":[105],"fit":[107],"implicit":[108],"representations,":[109],"optimization":[113],"employs":[115],"min-max":[117],"training":[118,182],"strategy":[119],"enhance":[121],"capability.":[124],"By":[125],"introducing":[126],"pre-training":[127],"strategies,":[128],"STAMP":[129,160],"can":[130,199],"be":[131,200],"adapted":[133],"systems":[136],"with":[137,145,157,232],"strong":[139],"generalization":[140],"ability.":[141],"Furthermore,":[142],"cope":[144],"practical":[147],"unlabeled":[148],"data":[149],"conditions,":[150],"propose":[152],"framework":[155,198,226,244],"compatible":[156],"not":[158],"only":[159],"but":[161],"also":[162],"other":[163],"advanced":[164],"models.":[166],"In":[167,240],"this":[168],"framework,":[169],"screening":[171],"process":[172],"first":[174],"conducted":[175],"by":[176,203],"traditional":[177],"generate":[180],"set":[183],"pseudo-normal":[185],"samples.":[186],"Second,":[187],"models":[189],"are":[190],"trained":[191],"then":[193],"used":[194],"for":[195,211],"detection.":[196],"The":[197],"further":[201],"optimized":[202],"performing":[204],"feature":[205],"selection":[206],"based":[207],"on":[208],"model-derived":[209],"information":[210],"better":[213],"detectability.":[214],"Extensive":[215],"experiments":[216],"real-world":[218],"datasets":[219],"demonstrate":[220],"that":[221],"proposed":[223],"model":[224],"achieve":[227],"superior":[228],"performance":[229],"when":[230],"compared":[231],"baselines":[233],"under":[234],"semi-supervised":[236],"conditions.":[239],"particular,":[241],"has":[245],"already":[246],"been":[247],"applied":[248],"Huawei's":[250],"GaussDB":[251],"(DWS)":[252],"system.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
