{"id":"https://openalex.org/W4205422493","doi":"https://doi.org/10.1109/bigdata52589.2021.9671550","title":"Gen<sup>2</sup>Out: Detecting and Ranking Generalized Anomalies","display_name":"Gen<sup>2</sup>Out: Detecting and Ranking Generalized Anomalies","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205422493","doi":"https://doi.org/10.1109/bigdata52589.2021.9671550"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671550","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"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/A5101634159","display_name":"Meng-Chieh Lee","orcid":"https://orcid.org/0000-0002-6271-8558"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Meng-Chieh Lee","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017630996","display_name":"Shubhranshu Shekhar","orcid":"https://orcid.org/0000-0002-1864-8302"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shubhranshu Shekhar","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069016136","display_name":"T. Noah Hutson","orcid":"https://orcid.org/0000-0002-8707-6838"},"institutions":[{"id":"https://openalex.org/I1324766304","display_name":"Barrow Neurological Institute","ror":"https://ror.org/01fwrsq33","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1324766304","https://openalex.org/I1325481341","https://openalex.org/I2802827176"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T. Noah Hutson","raw_affiliation_strings":["Barrow Neurological Institute"],"affiliations":[{"raw_affiliation_string":"Barrow Neurological Institute","institution_ids":["https://openalex.org/I1324766304"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048190465","display_name":"Leonidas Iasemidis","orcid":"https://orcid.org/0000-0001-5673-0596"},"institutions":[{"id":"https://openalex.org/I1324766304","display_name":"Barrow Neurological Institute","ror":"https://ror.org/01fwrsq33","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1324766304","https://openalex.org/I1325481341","https://openalex.org/I2802827176"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leon Iasemidis","raw_affiliation_strings":["Barrow Neurological Institute"],"affiliations":[{"raw_affiliation_string":"Barrow Neurological Institute","institution_ids":["https://openalex.org/I1324766304"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101634159"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.5026,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.6691905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"26","issue":null,"first_page":"801","last_page":"811"},"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":0.9979000091552734,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7111086845397949},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6564640998840332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5206177234649658},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5001115798950195},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4741479754447937},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.45818132162094116},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4016816318035126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.371934711933136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36474621295928955},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30561354756355286},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2684922218322754},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.24454641342163086}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7111086845397949},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6564640998840332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5206177234649658},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5001115798950195},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4741479754447937},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.45818132162094116},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4016816318035126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.371934711933136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36474621295928955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30561354756355286},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2684922218322754},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.24454641342163086},{"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":[{"id":"doi:10.1109/bigdata52589.2021.9671550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671550","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W187043655","https://openalex.org/W1242748811","https://openalex.org/W1513576273","https://openalex.org/W1964103254","https://openalex.org/W1986332411","https://openalex.org/W1995443851","https://openalex.org/W2015172091","https://openalex.org/W2019014808","https://openalex.org/W2026493302","https://openalex.org/W2052804984","https://openalex.org/W2091539146","https://openalex.org/W2122646361","https://openalex.org/W2148583977","https://openalex.org/W2163605009","https://openalex.org/W2170325549","https://openalex.org/W2187089797","https://openalex.org/W2296719434","https://openalex.org/W2472119793","https://openalex.org/W2559302039","https://openalex.org/W2740924709","https://openalex.org/W2792846986","https://openalex.org/W2796992128","https://openalex.org/W3035622304","https://openalex.org/W3155567600","https://openalex.org/W4239954780","https://openalex.org/W4254182148","https://openalex.org/W4294232646","https://openalex.org/W4298064392","https://openalex.org/W6607543680","https://openalex.org/W6630774086","https://openalex.org/W6657238249","https://openalex.org/W6684191040","https://openalex.org/W6684193916","https://openalex.org/W6685260713","https://openalex.org/W6720330311","https://openalex.org/W6750345437"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W4213170381","https://openalex.org/W3111802945","https://openalex.org/W2806741695","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258"],"abstract_inverted_index":{"In":[0],"a":[1,46,95,105,109,233],"cloud":[2],"of":[3,77,87,120,190],"m-dimensional":[4],"data":[5,230],"points,":[6],"how":[7],"would":[8],"we":[9,37,50],"spot,":[10],"as":[11,13,17,19,41,43,55,159,161,192],"well":[12,18,42,160],"rank,":[14],"both":[15,39,165],"single-point-":[16],"group-anomalies?":[20],"We":[21],"are":[22],"the":[23,121,126,132,137,149],"first":[24,33],"to":[25,53],"generalize":[26],"anomaly":[27,92,145],"detection":[28],"in":[29,71,84,97,155],"two":[30],"dimensions:":[31],"The":[32,58],"dimension":[34,60],"is":[35,61,131,172],"that":[36,62,135,147,156,203],"handle":[38],"point-anomalies,":[40],"group-anomalies,":[44,168],"under":[45],"unified":[47],"view":[48],"-":[49],"shall":[51],"refer":[52],"them":[54],"generalized":[56,163],"anomalies.":[57],"second":[59],"Gen<sup>2</sup>Out":[63,133,191,204],"not":[64],"only":[65],"detects,":[66,158],"but":[67],"also":[68],"ranks,":[69],"anomalies":[70,78],"suspiciousness":[72],"order.":[73],"Detection,":[74],"and":[75,143,167,174,222],"ranking,":[76],"has":[79,136],"numerous":[80],"applications:":[81],"For":[82],"example,":[83],"EEG":[85],"recordings":[86,186],"an":[88,91],"epileptic":[89,185],"patient,":[90],"may":[93,103],"indicate":[94],"seizure;":[96],"computer":[98],"network":[99],"traffic":[100],"data,":[101],"it":[102,157,171],"signify":[104],"power":[106],"failure,":[107],"or":[108,210],"DoS/DDoS":[110],"attack.We":[111],"start":[112],"by":[113,194],"setting":[114],"some":[115],"reasonable":[116],"axioms;":[117],"surprisingly,":[118],"none":[119],"earlier":[122],"methods":[123],"pass":[124],"all":[125],"axioms.":[127],"Our":[128],"main":[129],"contribution":[130],"algorithm,":[134],"following":[138],"desirable":[139],"properties:":[140],"(a)":[141],"Principled":[142],"Sound":[144],"scoring":[146],"obeys":[148],"axioms":[150],"for":[151,220,227],"detectors,":[152],"(b)":[153],"Doubly-general":[154],"ranks":[162],"anomaly&#x2013;":[164],"point-":[166],"(c)":[169],"Scalable,":[170],"fast":[173],"scalable,":[175],"linear":[176],"on":[177,183,197,215,232],"input":[178],"size.":[179],"(d)":[180],"Effective,":[181],"experiments":[182],"real-world":[184,199],"(200GB)":[187],"demonstrate":[188],"effectiveness":[189],"confirmed":[193],"clinicians.":[195],"Experiments":[196],"27":[198],"benchmark":[200],"datasets":[201],"show":[202],"detects":[205],"ground":[206],"truth":[207],"groups,":[208],"matches":[209],"outperforms":[211],"point-anomaly":[212],"baseline":[213],"algorithms":[214],"accuracy,":[216],"with":[217],"no":[218],"competition":[219],"group-anomalies":[221],"requires":[223],"about":[224],"2":[225],"minutes":[226],"1":[228],"million":[229],"points":[231],"stock":[234],"machine.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}