{"id":"https://openalex.org/W2954523198","doi":"https://doi.org/10.14778/3324301.3324308","title":"Efficient discovery of sequence outlier patterns","display_name":"Efficient discovery of sequence outlier patterns","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2954523198","doi":"https://doi.org/10.14778/3324301.3324308","mag":"2954523198"},"language":"en","primary_location":{"id":"doi:10.14778/3324301.3324308","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3324301.3324308","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1721.1/136517","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049926126","display_name":"Lei Cao","orcid":"https://orcid.org/0000-0001-9909-8607"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lei Cao","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108577922","display_name":"Yizhou Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhou Yan","raw_affiliation_strings":["Worcester Polytechnic Institute Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037742794","display_name":"Samuel Madden","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Madden","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008269094","display_name":"Elke A. Rundensteiner","orcid":"https://orcid.org/0000-0001-5375-9254"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elke A. Rundensteiner","raw_affiliation_strings":["Worcester Polytechnic Institute Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019380854","display_name":"Mathan Gopalsamy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210163196","display_name":"Signify (Netherlands)","ror":"https://ror.org/0532vdr17","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210163196"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mathan Gopalsamy","raw_affiliation_strings":["Signify Research, Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signify Research, Cambridge","institution_ids":["https://openalex.org/I4210163196"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5049926126"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":3.1734,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93035242,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"12","issue":"8","first_page":"920","last_page":"932"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9976999759674072,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996999979019165,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7434079647064209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7122746109962463},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6663350462913513},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6470068693161011},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6178006529808044},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5600693821907043},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5474976301193237},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5255391001701355},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4945943057537079},{"id":"https://openalex.org/keywords/k-optimal-pattern-discovery","display_name":"K-optimal pattern discovery","score":0.47680923342704773},{"id":"https://openalex.org/keywords/pattern-language","display_name":"Pattern language (formal languages)","score":0.41723668575286865},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41260042786598206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38209009170532227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3631463050842285},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.24334925413131714},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22886872291564941},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10988953709602356}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7434079647064209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7122746109962463},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6663350462913513},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6470068693161011},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6178006529808044},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5600693821907043},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5474976301193237},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5255391001701355},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4945943057537079},{"id":"https://openalex.org/C105445830","wikidata":"https://www.wikidata.org/wiki/Q6322855","display_name":"K-optimal pattern discovery","level":3,"score":0.47680923342704773},{"id":"https://openalex.org/C2776362478","wikidata":"https://www.wikidata.org/wiki/Q17156908","display_name":"Pattern language (formal languages)","level":2,"score":0.41723668575286865},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41260042786598206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38209009170532227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3631463050842285},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.24334925413131714},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22886872291564941},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10988953709602356},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3324301.3324308","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3324301.3324308","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/136517","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/136517","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"VLDB Endowment","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/136517","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/136517","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"VLDB Endowment","raw_type":"http://purl.org/eprint/type/ConferencePaper"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W325970","https://openalex.org/W50012472","https://openalex.org/W132885894","https://openalex.org/W1488856884","https://openalex.org/W1584003909","https://openalex.org/W1587157435","https://openalex.org/W1608194207","https://openalex.org/W1641039719","https://openalex.org/W1676985236","https://openalex.org/W1941427975","https://openalex.org/W1966969112","https://openalex.org/W1971697515","https://openalex.org/W1979180881","https://openalex.org/W1990800901","https://openalex.org/W1995003166","https://openalex.org/W2038819732","https://openalex.org/W2068383400","https://openalex.org/W2095924956","https://openalex.org/W2098514508","https://openalex.org/W2105510466","https://openalex.org/W2112971308","https://openalex.org/W2113049098","https://openalex.org/W2129860818","https://openalex.org/W2140369911","https://openalex.org/W2147694185","https://openalex.org/W2153567088","https://openalex.org/W2153982189","https://openalex.org/W2155490300","https://openalex.org/W2156026066","https://openalex.org/W2158454296","https://openalex.org/W2167917691","https://openalex.org/W2259693656","https://openalex.org/W2272376083","https://openalex.org/W2360114527","https://openalex.org/W2545117753","https://openalex.org/W2613683361","https://openalex.org/W2897997001","https://openalex.org/W3150565720","https://openalex.org/W4254829975","https://openalex.org/W4288598360","https://openalex.org/W6755395053"],"related_works":["https://openalex.org/W2002417865","https://openalex.org/W1595351371","https://openalex.org/W3116762327","https://openalex.org/W2230433129","https://openalex.org/W2606848831","https://openalex.org/W2390515779","https://openalex.org/W2029968811","https://openalex.org/W2001734931","https://openalex.org/W2127481958","https://openalex.org/W4205716295"],"abstract_inverted_index":{"Modern":[0],"Internet":[1],"of":[2,11,15,20,46,57,75,83,93,97,124,141,176,194,204,216],"Things":[3],"(":[4],"IoT":[5,186],")":[6],"applications":[7],"generate":[8],"massive":[9],"amounts":[10],"time-stamped":[12],"data,":[13],"much":[14],"it":[16,197],"in":[17,102,127,138,183],"the":[18,43,72,91,105,122,125,139,142,174,192],"form":[19],"discrete,":[21],"symbolic":[22],"sequences.":[23,40],"In":[24,100],"this":[25,94,213],"work,":[26],"we":[27],"present":[28,87],"a":[29,76,129],"new":[30,63,95,214],"system":[31],"called":[32],"TOP":[33,61,177],"that":[34,51,70],"de&lt;u&gt;T&lt;/u&gt;ects":[35],"&lt;u&gt;O&lt;/u&gt;utlier":[36],"&lt;u&gt;P&lt;/u&gt;atterns":[37],"from":[38,78],"these":[39],"To":[41],"solve":[42],"fundamental":[44],"limitation":[45],"existing":[47],"pattern":[48,64,77,110,119,130,158,168,225],"mining":[49,92,111,210,226],"semantics":[50,65],"miss":[52],"outlier":[53,157,181,218,224],"patterns":[54,69,182],"hidden":[55],"inside":[56],"larger":[58],"frequent":[59],"patterns,":[60,219],"offers":[62],"based":[66],"on":[67],"contextual":[68,98,167,217],"distinguish":[71],"independent":[73],"occurrence":[74,80],"its":[79,84],"as":[81,149],"part":[82],"super-pattern.":[85],"We":[86,189],"efficient":[88],"algorithms":[89],"for":[90,108],"class":[96,215],"patterns.":[99],"particular,":[101],"contrast":[103],"to":[104,163,198,201,211,222,227],"bottom-up":[106],"strategy":[107,116],"state-of-the-art":[109,209],"techniques,":[112],"our":[113],"top-down":[114],"Reduce":[115],"piggy":[117],"backs":[118],"detection":[120,123,159],"with":[121],"context":[126],"which":[128],"occurs.":[131],"Our":[132,170],"approach":[133],"achieves":[134],"linear":[135],"time":[136],"complexity":[137],"length":[140],"input":[143],"sequence.":[144],"Effective":[145],"optimization":[146],"techniques":[147],"such":[148],"context-driven":[150],"search":[151],"space":[152],"pruning":[153],"and":[154],"inverted":[155],"index-based":[156],"are":[160],"also":[161,190],"proposed":[162],"further":[164],"speed":[165],"up":[166,200],"mining.":[169],"experimental":[171],"evaluation":[172],"demonstrates":[173],"effectiveness":[175],"at":[178],"capturing":[179],"meaningful":[180],"several":[184],"real-world":[185],"use":[187],"cases.":[188],"demonstrate":[191],"efficiency":[193],"TOP,":[195],"showing":[196],"be":[199],"2":[202],"orders":[203],"magnitude":[205],"faster":[206],"than":[207],"adapting":[208],"produce":[212],"allowing":[220],"us":[221],"scale":[223],"large":[228],"sequence":[229],"datasets.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
