{"id":"https://openalex.org/W4388041271","doi":"https://doi.org/10.1145/3620678.3624655","title":"Multivariate Anomaly Detection with Domain Clustering","display_name":"Multivariate Anomaly Detection with Domain Clustering","publication_year":2023,"publication_date":"2023-10-30","ids":{"openalex":"https://openalex.org/W4388041271","doi":"https://doi.org/10.1145/3620678.3624655"},"language":"en","primary_location":{"id":"doi:10.1145/3620678.3624655","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3620678.3624655","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3620678.3624655","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Symposium on Cloud Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3620678.3624655","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054533062","display_name":"Frederic Boesel","orcid":"https://orcid.org/0009-0006-4773-7554"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Frederic Boesel","raw_affiliation_strings":["IBM Research, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Livio Schl\u00e4pfer","orcid":"https://orcid.org/0009-0008-1586-8650"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Livio Schl\u00e4pfer","raw_affiliation_strings":["IBM Research, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007575599","display_name":"Haralampos Pozidis","orcid":"https://orcid.org/0000-0001-5084-6651"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Haris Pozidis","raw_affiliation_strings":["IBM Research, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023490857","display_name":"Mitch Gusat","orcid":"https://orcid.org/0009-0005-4500-1618"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Mitch Gusat","raw_affiliation_strings":["IBM Research, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054533062"],"corresponding_institution_ids":["https://openalex.org/I4210126328"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13880884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"193","last_page":"199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9952999949455261,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8160281181335449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7481889724731445},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.7260936498641968},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6191375851631165},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5779495239257812},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5240678787231445},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.496952086687088},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.496368944644928},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.45028337836265564},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43925800919532776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3808898329734802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35848289728164673}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8160281181335449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7481889724731445},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7260936498641968},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6191375851631165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5779495239257812},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5240678787231445},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.496952086687088},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.496368944644928},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.45028337836265564},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43925800919532776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3808898329734802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35848289728164673},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3620678.3624655","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3620678.3624655","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3620678.3624655","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Symposium on Cloud Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3620678.3624655","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3620678.3624655","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3620678.3624655","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Symposium on Cloud Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388041271.pdf","grobid_xml":"https://content.openalex.org/works/W4388041271.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2049017883","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2493916176","https://openalex.org/W2785362611","https://openalex.org/W2912126994","https://openalex.org/W2950361482","https://openalex.org/W3006450303","https://openalex.org/W3042046988","https://openalex.org/W3098957257","https://openalex.org/W3100839026","https://openalex.org/W3102650215","https://openalex.org/W3137919061","https://openalex.org/W3170937175","https://openalex.org/W3177049011","https://openalex.org/W3198059351","https://openalex.org/W4285176337","https://openalex.org/W4302013153"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W1570805059","https://openalex.org/W2357266745","https://openalex.org/W1578824628","https://openalex.org/W4390961098","https://openalex.org/W4285233543","https://openalex.org/W4230838436"],"abstract_inverted_index":{"Existing":[0],"time-series":[1,80,100,127,138],"anomaly":[2,76],"detection":[3,77],"(AD)":[4],"pipelines":[5],"for":[6,42,73],"cloud":[7,17,20,94],"monitoring":[8,96,165],"at":[9],"scale":[10],"commonly":[11],"rely":[12],"on":[13,56,78,110,123,136],"isolated":[14,111],"training":[15,178],"per":[16],"service":[18],"or":[19],"infrastructure":[21,95,103,169],"component.":[22,148],"However,":[23],"with":[24,158],"the":[25,57,91,114,137,172,175],"increasing":[26],"volume":[27],"of":[28,33,59,93,160,167,183],"data":[29,60,139],"generated":[30],"from":[31],"thousands":[32,166],"services":[34],"and":[35,120,125,180],"components,":[36,142],"there":[37],"is":[38],"an":[39,68],"untapped":[40],"opportunity":[41],"a":[43,132,146],"more":[44],"effective":[45],"approach":[46],"to":[47,85,97,106,145,162],"detect":[48],"key":[49],"performance":[50,182],"indicator":[51],"(KPI)":[52],"anomalies":[53],"by":[54,130],"capitalizing":[55],"abundance":[58],"available.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"propose":[66],"MADDoC,":[67],"unsupervised":[69,99],"transfer":[70,186],"learning":[71,131,187],"framework":[72,116,173],"reconstruction":[74,134],"based":[75],"multivariate":[79],"data.":[81],"We":[82],"show":[83],"how":[84],"efficiently":[86],"leverage":[87],"available":[88],"KPIs":[89],"in":[90,155],"realm":[92],"generalize":[98],"AD":[101,128,181,185],"across":[102,140],"components.":[104,170],"Compared":[105],"state-of-the-art":[107],"approaches":[108],"relying":[109],"component-wise":[112],"training,":[113,157],"MADDoC":[115,150],"achieves":[117,151],"superior":[118],"Precision":[119],"F1":[121],"scores":[122],"public":[124],"internal":[126],"datasets,":[129],"strong":[133],"backbone":[135],"many":[141],"before":[143],"fine-tuning":[144],"specific":[147],"Moreover,":[149],"substantial":[152],"cost":[153],"savings":[154],"model":[156],"reductions":[159],"60%":[161],"75%":[163],"when":[164],"storage":[168],"Further,":[171],"overcomes":[174],"trade-off":[176],"between":[177],"efficiency":[179],"previous":[184],"approaches.":[188]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
