{"id":"https://openalex.org/W2870019921","doi":"https://doi.org/10.1109/noms.2018.8406251","title":"Transfer learning for server behavior classification in small IT environments","display_name":"Transfer learning for server behavior classification in small IT environments","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2870019921","doi":"https://doi.org/10.1109/noms.2018.8406251","mag":"2870019921"},"language":"en","primary_location":{"id":"doi:10.1109/noms.2018.8406251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms.2018.8406251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5018114555","display_name":"Jasmina Bogojeska","orcid":"https://orcid.org/0000-0001-5634-7984"},"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":"Jasmina Bogojeska","raw_affiliation_strings":["IBM Research-Zurich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research-Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082313228","display_name":"D. Wiesmann","orcid":null},"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":"Dorothea Wiesmann","raw_affiliation_strings":["IBM Research-Zurich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research-Zurich, Switzerland","institution_ids":["https://openalex.org/I4210126328"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210126328"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9714999794960022,"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"}},"topics":[{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9714999794960022,"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/T12127","display_name":"Software System Performance and Reliability","score":0.9686999917030334,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9628999829292297,"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/computer-science","display_name":"Computer science","score":0.863351047039032},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.5439077615737915},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5068126320838928},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.4992039203643799},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.4960828721523285},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46659186482429504},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.45876118540763855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.456378310918808},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4533185660839081},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4350699186325073},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4335366487503052},{"id":"https://openalex.org/keywords/ticket","display_name":"Ticket","score":0.426943302154541},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4148308038711548},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4117542505264282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3606746196746826},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.12464189529418945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.863351047039032},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5439077615737915},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5068126320838928},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.4992039203643799},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4960828721523285},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46659186482429504},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.45876118540763855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.456378310918808},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4533185660839081},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4350699186325073},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4335366487503052},{"id":"https://openalex.org/C2776540713","wikidata":"https://www.wikidata.org/wiki/Q7800647","display_name":"Ticket","level":2,"score":0.426943302154541},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4148308038711548},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4117542505264282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3606746196746826},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.12464189529418945},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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:10.1109/noms.2018.8406251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms.2018.8406251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1519342765","https://openalex.org/W1560550898","https://openalex.org/W1565327149","https://openalex.org/W1615417444","https://openalex.org/W1731081199","https://openalex.org/W1871180460","https://openalex.org/W1966026565","https://openalex.org/W1971206108","https://openalex.org/W2057376540","https://openalex.org/W2065180801","https://openalex.org/W2070996757","https://openalex.org/W2096873754","https://openalex.org/W2099971677","https://openalex.org/W2119595900","https://openalex.org/W2131479143","https://openalex.org/W2133491790","https://openalex.org/W2143104527","https://openalex.org/W2144752499","https://openalex.org/W2148522164","https://openalex.org/W2149889242","https://openalex.org/W2152231303","https://openalex.org/W2165698076","https://openalex.org/W2261310161","https://openalex.org/W2511131004","https://openalex.org/W2596699592","https://openalex.org/W2787894218","https://openalex.org/W2911964244","https://openalex.org/W2949664970","https://openalex.org/W2953127297","https://openalex.org/W2964121793","https://openalex.org/W4285719527","https://openalex.org/W6631028693","https://openalex.org/W6633555322","https://openalex.org/W6633949838","https://openalex.org/W6636466485","https://openalex.org/W6636918297","https://openalex.org/W6639239318","https://openalex.org/W6640290990","https://openalex.org/W6674764686","https://openalex.org/W6677658955","https://openalex.org/W6679468046","https://openalex.org/W6681414149","https://openalex.org/W6682003056","https://openalex.org/W6682738034","https://openalex.org/W6692729736","https://openalex.org/W6725448924","https://openalex.org/W6735295124","https://openalex.org/W7062423369"],"related_works":["https://openalex.org/W132856376","https://openalex.org/W4288388931","https://openalex.org/W2892636954","https://openalex.org/W4206805925","https://openalex.org/W4375841483","https://openalex.org/W2022874741","https://openalex.org/W2052515325","https://openalex.org/W2364431604","https://openalex.org/W2018860124","https://openalex.org/W3112526189"],"abstract_inverted_index":{"Technology":[0],"refresh":[1,34],"is":[2,29,76,141],"an":[3],"important":[4],"component":[5],"in":[6,71,108],"data-center":[7],"management":[8],"that":[9,47,101,121,128,160],"needs":[10],"to":[11,30,143],"be":[12],"properly":[13,123],"justified":[14],"because":[15],"of":[16,26,80,131,134,147,184,191],"its":[17],"high":[18],"cost":[19],"and":[20,50,61],"associated":[21],"migration":[22],"risk.":[23],"The":[24],"goal":[25],"this":[27,154],"paper":[28],"support":[31],"the":[32,66,84,129,132,138,144,148,162,180],"technology":[33],"decision":[35],"process":[36],"for":[37,78,89,116,174],"small":[38,85,149,175],"target":[39,118,145,150],"IT":[40,67,91,106,119,151,168,176],"environments":[41,68,107,169],"with":[42,54],"a":[43,73,95,109,114,156,188],"statistical":[44],"learning":[45,99],"method":[46],"automatically":[48],"identifies":[49],"ranks":[51],"their":[52],"servers":[53],"problematic":[55],"behavior":[56],"based":[57],"on":[58,187],"incident":[59],"ticket":[60],"server":[62],"attribute":[63],"data.":[64,193],"Since":[65],"are":[69],"heterogeneous,":[70],"practice,":[72],"separate":[74],"model":[75,115,159,186],"trained":[77],"each":[79,117],"them.":[81],"To":[82],"address":[83],"sample":[86],"sizes":[87],"available":[88,164],"many":[90,166],"environments,":[92],"we":[93],"develop":[94],"random":[96],"forest":[97],"transfer":[98],"solution":[100],"leverages":[102],"information":[103,163],"from":[104,137,165],"large":[105,139,167,189],"selective":[110],"manner.":[111],"It":[112],"trains":[113],"environment":[120],"uses":[122,161],"derived":[124],"resampling":[125],"weights":[126],"such":[127],"distribution":[130,146],"pool":[133],"all":[135],"examples":[136],"accounts":[140],"matched":[142],"environment.":[152],"In":[153],"way,":[155],"tailored":[157],"predictive":[158],"provides":[170],"good":[171],"quality":[172,183],"predictions":[173],"environments.":[177],"We":[178],"demonstrate":[179],"superior":[181],"prediction":[182],"our":[185],"set":[190],"real":[192]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
