{"id":"https://openalex.org/W3006813586","doi":"https://doi.org/10.1109/bigdata47090.2019.9005642","title":"Large Scale Time Series Analysis for Infrastructure Reliability","display_name":"Large Scale Time Series Analysis for Infrastructure Reliability","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3006813586","doi":"https://doi.org/10.1109/bigdata47090.2019.9005642","mag":"3006813586"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005642","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005642","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5106698700","display_name":"Zhichao Wang","orcid":"https://orcid.org/0000-0001-8075-1784"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhichao Wang","raw_affiliation_strings":["Facebook, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Facebook, Seattle, USA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023261067","display_name":"Singh Shashank","orcid":"https://orcid.org/0009-0002-4642-9753"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shashank Singh","raw_affiliation_strings":["Facebook, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Facebook, Menlo Park, USA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009020394","display_name":"Pereira Arnold","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arnold Pereira","raw_affiliation_strings":["Facebook, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Facebook, Menlo Park, USA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I4210099336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5106698700"],"corresponding_institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I58610484"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.19664508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"6240","last_page":"6242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9968000054359436,"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":0.9937999844551086,"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":0.8306005597114563},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6835102438926697},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6429921984672546},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5303757190704346},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.5005829334259033},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47578489780426025},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.4740055501461029},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46506431698799133},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4650622010231018},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43410539627075195},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4212014079093933},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.41464537382125854},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3336276710033417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3013795018196106},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.22535085678100586},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.20340079069137573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8306005597114563},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6835102438926697},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6429921984672546},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5303757190704346},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.5005829334259033},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47578489780426025},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.4740055501461029},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46506431698799133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4650622010231018},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43410539627075195},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4212014079093933},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.41464537382125854},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3336276710033417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3013795018196106},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.22535085678100586},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.20340079069137573},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005642","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005642","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1536447791","https://openalex.org/W2041184937","https://openalex.org/W2079560958","https://openalex.org/W2091860924","https://openalex.org/W2278984902","https://openalex.org/W2904285708","https://openalex.org/W4254182148","https://openalex.org/W4297814361","https://openalex.org/W6632102171","https://openalex.org/W6720514713","https://openalex.org/W6757174574"],"related_works":["https://openalex.org/W695875","https://openalex.org/W2049211","https://openalex.org/W8717022","https://openalex.org/W11991885","https://openalex.org/W9869893","https://openalex.org/W13690516","https://openalex.org/W14017884","https://openalex.org/W4684108","https://openalex.org/W11244355","https://openalex.org/W11642900"],"abstract_inverted_index":{"Performance,":[0],"reliability":[1],"and":[2,53,75,93,125,153,184],"efficiency":[3],"of":[4,27,33,39,60,115,127,140],"infrastructure":[5,29],"systems":[6],"are":[7,77],"instrumental":[8],"to":[9,23,69,79,101,131],"high":[10],"quality":[11,158],"user":[12],"experience":[13],"at":[14,83],"Facebook.":[15],"Extensive":[16],"time":[17,85,90,116],"series":[18,86,91],"logging":[19],"has":[20],"been":[21],"implemented":[22],"monitor":[24],"the":[25,81,123,141,145,167],"health":[26],"various":[28],"systems.":[30],"CPU":[31],"usage":[32],"internal":[34],"services":[35],"(e.g.,":[36],"large":[37,113],"number":[38],"machine":[40,149],"learning":[41,150],"models":[42],"built":[43],"by":[44,64,176],"different":[45],"teams),":[46],"network":[47],"traffic":[48],"between":[49],"internet":[50],"service":[51],"providers":[52],"Facebook":[54,161],"data":[55],"centers,":[56],"video":[57,156],"streaming":[58],"KPI":[59],"live":[61,162],"broadcasts":[62],"generated":[63],"daily":[65],"active":[66],"users,":[67],"just":[68],"name":[70],"a":[71,112,128,178],"few.":[72],"Many":[73],"methods":[74],"tools":[76,171],"available":[78],"analyze":[80],"logs":[82],"single":[84],"level,":[87],"for":[88,148],"instance,":[89],"forecasting":[92],"(contextual)":[94],"anomaly":[95],"detection.":[96],"Relatively":[97],"little":[98],"is":[99,181],"done":[100],"address":[102],"an":[103],"emerging":[104],"use":[105],"case":[106],"-":[107],"identifying":[108],"similar/outlier":[109],"instances":[110],"from":[111],"collection":[114],"series.":[117],"In":[118],"this":[119],"poster,":[120],"we":[121,165],"demonstrate":[122],"design":[124],"implementation":[126],"generic":[129],"framework":[130],"accomplish":[132],"such":[133],"needs.":[134],"We":[135],"discuss":[136],"two":[137],"real-world":[138],"implementations":[139],"approach:":[142],"(i)":[143],"auto-scaling":[144],"inference":[146],"capacity":[147],"(ML)":[151],"models,":[152],"(ii)":[154],"detect":[155],"ingestion":[157],"outliers":[159],"across":[160],"broadcasts.":[163],"Specifically,":[164],"fill":[166],"gaps":[168],"in":[169],"existing":[170],"(and/or":[172],"external,":[173],"off-the-shelf":[174],"alternatives)":[175],"proposing":[177],"method":[179],"which":[180],"interpretable,":[182],"generalizable":[183],"scalable.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}