{"id":"https://openalex.org/W2884123408","doi":"https://doi.org/10.1145/3219819.3220107","title":"xStream","display_name":"xStream","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2884123408","doi":"https://doi.org/10.1145/3219819.3220107","mag":"2884123408"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3220107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220107","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220107","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023417624","display_name":"Emaad Manzoor","orcid":"https://orcid.org/0000-0003-3187-9719"},"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":"Emaad Manzoor","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028407723","display_name":"Hemank Lamba","orcid":"https://orcid.org/0000-0002-9794-3587"},"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":"Hemank Lamba","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001634795","display_name":"Leman Akoglu","orcid":null},"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":"Leman Akoglu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":84,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1963","last_page":"1972"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9902999997138977,"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":0.9883000254631042,"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":0.7682723999023438},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6638998985290527},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.660546064376831},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6353795528411865},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6168316602706909},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5721245408058167},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5352231860160828},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5338894724845886},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4784533977508545},{"id":"https://openalex.org/keywords/streaming-algorithm","display_name":"Streaming algorithm","score":0.47840794920921326},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45340201258659363},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4312777519226074},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4234219491481781},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3425159454345703},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30805832147598267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1525876522064209},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13649150729179382},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.10547292232513428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682723999023438},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6638998985290527},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.660546064376831},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6353795528411865},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6168316602706909},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5721245408058167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5352231860160828},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5338894724845886},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4784533977508545},{"id":"https://openalex.org/C187166803","wikidata":"https://www.wikidata.org/wiki/Q2835831","display_name":"Streaming algorithm","level":3,"score":0.47840794920921326},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45340201258659363},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4312777519226074},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4234219491481781},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3425159454345703},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30805832147598267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1525876522064209},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13649150729179382},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.10547292232513428},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219819.3220107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220107","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3219819.3220107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220107","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1617052614","display_name":null,"funder_award_id":"CAREER 1452425, IIS 1408287","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5512748762","display_name":"CAREER: A General Framework for Methodical and Interpretable Anomaly Mining","funder_award_id":"1452425","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6537471220","display_name":"III: Medium: Collaborative Research: Collective Opinion Fraud Detection: Identifying and Integrating Cues from Language, Behavior, and Networks","funder_award_id":"1408287","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2884123408.pdf","grobid_xml":"https://content.openalex.org/works/W2884123408.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W150715854","https://openalex.org/W201138236","https://openalex.org/W309312769","https://openalex.org/W1242748811","https://openalex.org/W1520833380","https://openalex.org/W1565746575","https://openalex.org/W1566520830","https://openalex.org/W1975188201","https://openalex.org/W1990106316","https://openalex.org/W2000661457","https://openalex.org/W2011388517","https://openalex.org/W2015887370","https://openalex.org/W2026493302","https://openalex.org/W2037757210","https://openalex.org/W2056081083","https://openalex.org/W2061122559","https://openalex.org/W2080234606","https://openalex.org/W2097714558","https://openalex.org/W2101549186","https://openalex.org/W2110784166","https://openalex.org/W2116300222","https://openalex.org/W2122646361","https://openalex.org/W2144182447","https://openalex.org/W2147717514","https://openalex.org/W2256894031","https://openalex.org/W2284900416","https://openalex.org/W2295268030","https://openalex.org/W2296719434","https://openalex.org/W2338990760","https://openalex.org/W2472119793","https://openalex.org/W2584316068","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2962766560","https://openalex.org/W2963210896","https://openalex.org/W2979473749","https://openalex.org/W3099531968","https://openalex.org/W4211139099","https://openalex.org/W4254420769"],"related_works":["https://openalex.org/W2108660516","https://openalex.org/W2606848831","https://openalex.org/W2029968811","https://openalex.org/W3089017057","https://openalex.org/W1595351371","https://openalex.org/W2950134958","https://openalex.org/W2548275785","https://openalex.org/W2152576712","https://openalex.org/W1427871279","https://openalex.org/W4312370755"],"abstract_inverted_index":{"This":[0,42],"work":[1],"addresses":[2],"the":[3,75,117,125,130],"outlier":[4,63,126],"detection":[5,127],"problem":[6,128],"for":[7,67,129,193],"feature-evolving":[8,155],"streams,":[9,195],"which":[10,73,197],"has":[11,74],"not":[12],"been":[13],"studied":[14],"before.":[15],"In":[16,120],"this":[17,68],"setting":[18,72],"both":[19],"(1)":[20,79],"data":[21],"points":[22,49],"may":[23,35],"evolve,":[24,36],"with":[25,37,50,177,189],"feature":[26,33],"values":[27],"changing,":[28],"as":[29,31,116,135,137,167,169],"well":[30,136],"(2)":[32,90],"space":[34,191],"newly-emerging":[38],"features":[39,52],"over":[40],"time.":[41,57],"is":[43,81,166,185],"notably":[44],"different":[45],"from":[46],"row-streams,":[47],"where":[48],"fixed":[51],"arrive":[53],"one":[54],"at":[55,94],"a":[56,60,82],"We":[58,140,179],"propose":[59],"density-based":[61],"ensemble":[62],"detector,":[64],"called":[65],"xStream,":[66],"more":[69],"extreme":[70],"streaming":[71],"following":[76],"key":[77],"properties:":[78],"it":[80,91,99],"constant-space":[83],"and":[84,109,154,160,172,187],"constant-time":[85],"(per":[86],"incoming":[87],"update)":[88],"algorithm,":[89],"measures":[92],"outlierness":[93],"multiple":[95],"scales":[96],"or":[97],"granularities,":[98],"can":[100,123],"handle":[101],"(3":[102],"i":[103],")":[104],"high-dimensionality":[105],"through":[106],"distance-preserving":[107],"projections,":[108],"(3$ii$)":[110],"non-stationarity":[111],"via":[112],"$O(1)$-time":[113],"model":[114],"updates":[115],"stream":[118],"progresses.":[119],"addition,":[121],"xStream":[122,142,165],"address":[124],"(less":[131],"general)":[132],"disk-resident":[133],"static":[134,159],"row-streaming":[138,161],"settings.":[139],"evaluate":[141],"rigorously":[143],"on":[144,196],"numerous":[145],"real-life":[146],"datasets":[147],"in":[148,175],"all":[149],"three":[150],"settings:":[151],"static,":[152],"row-stream,":[153],"stream.":[156],"Experiments":[157],"under":[158],"scenarios":[162],"show":[163],"that":[164,182],"competitive":[168],"state-of-the-art":[170],"detectors":[171],"particularly":[173],"effective":[174],"high-dimensions":[176],"noise.":[178],"also":[180],"demonstrate":[181],"our":[183],"solution":[184],"fast":[186],"accurate":[188],"modest":[190],"overhead":[192],"evolving":[194],"there":[198],"exists":[199],"no":[200],"competition.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2018-08-03T00:00:00"}
