{"id":"https://openalex.org/W2334510217","doi":"https://doi.org/10.1109/siu.2015.7130049","title":"Network traffic estimation using Markov chain and Incremental Gaussian Mixture","display_name":"Network traffic estimation using Markov chain and Incremental Gaussian Mixture","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W2334510217","doi":"https://doi.org/10.1109/siu.2015.7130049","mag":"2334510217"},"language":"en","primary_location":{"id":"doi:10.1109/siu.2015.7130049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2015.7130049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23nd Signal Processing and Communications Applications Conference (SIU)","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/A5064137202","display_name":"Deniz Kumlu","orcid":"https://orcid.org/0000-0002-7192-7466"},"institutions":[{"id":"https://openalex.org/I157637111","display_name":"Naval Academy","ror":"https://ror.org/05syseh24","country_code":"TR","type":"education","lineage":["https://openalex.org/I157637111"]},{"id":"https://openalex.org/I48912391","display_name":"Istanbul Technical University","ror":"https://ror.org/059636586","country_code":"TR","type":"education","lineage":["https://openalex.org/I48912391"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Deniz Kumlu","raw_affiliation_strings":["Deniz Harp Okulu, Tuzla, \u0130stanbul","\u0130stanbul Teknik \u00dcniversitesi, Maslak, \u0130stanbul"],"affiliations":[{"raw_affiliation_string":"Deniz Harp Okulu, Tuzla, \u0130stanbul","institution_ids":["https://openalex.org/I157637111"]},{"raw_affiliation_string":"\u0130stanbul Teknik \u00dcniversitesi, Maslak, \u0130stanbul","institution_ids":["https://openalex.org/I48912391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026068372","display_name":"\u0130brahim H\u00f6kelek","orcid":"https://orcid.org/0000-0003-0729-178X"},"institutions":[{"id":"https://openalex.org/I48912391","display_name":"Istanbul Technical University","ror":"https://ror.org/059636586","country_code":"TR","type":"education","lineage":["https://openalex.org/I48912391"]},{"id":"https://openalex.org/I4210141511","display_name":"TUBITAK BILGEM","ror":"https://ror.org/057kvja37","country_code":"TR","type":"government","lineage":["https://openalex.org/I4210141511"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ibrahim Hokelek","raw_affiliation_strings":["T\u00dcB\u0130TAK B\u0130LGEM, Gebze, Kocaeli","\u0130stanbul Teknik \u00dcniversitesi, Maslak, \u0130stanbul"],"affiliations":[{"raw_affiliation_string":"T\u00dcB\u0130TAK B\u0130LGEM, Gebze, Kocaeli","institution_ids":["https://openalex.org/I4210141511"]},{"raw_affiliation_string":"\u0130stanbul Teknik \u00dcniversitesi, Maslak, \u0130stanbul","institution_ids":["https://openalex.org/I48912391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5064137202"],"corresponding_institution_ids":["https://openalex.org/I157637111","https://openalex.org/I48912391"],"apc_list":null,"apc_paid":null,"fwci":0.7716,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78876794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1187","last_page":"1190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9915000200271606,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9901000261306763,"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/markov-chain","display_name":"Markov chain","score":0.7737552523612976},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6215353012084961},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.597082793712616},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5615149140357971},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.561112642288208},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5098595023155212},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4938562512397766},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4597700834274292},{"id":"https://openalex.org/keywords/additive-markov-chain","display_name":"Additive Markov chain","score":0.4148738384246826},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3681807219982147},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.34128057956695557},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2939609885215759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26035624742507935},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21968621015548706},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21285325288772583}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7737552523612976},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6215353012084961},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597082793712616},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5615149140357971},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.561112642288208},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5098595023155212},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4938562512397766},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4597700834274292},{"id":"https://openalex.org/C96810086","wikidata":"https://www.wikidata.org/wiki/Q17003273","display_name":"Additive Markov chain","level":5,"score":0.4148738384246826},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3681807219982147},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.34128057956695557},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2939609885215759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26035624742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21968621015548706},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21285325288772583},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/siu.2015.7130049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2015.7130049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23nd Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},{"id":"pmh:oai:polen.itu.edu.tr:11527/55308","is_oa":false,"landing_page_url":"https://hdl.handle.net/11527/55308","pdf_url":null,"source":{"id":"https://openalex.org/S4306400460","display_name":"Istanbul Technical University Academic Open Archive (Istanbul Technical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I48912391","host_organization_name":"Istanbul Technical University","host_organization_lineage":["https://openalex.org/I48912391"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1996102080","https://openalex.org/W2056552633","https://openalex.org/W2121313689","https://openalex.org/W2999389558","https://openalex.org/W6678186859"],"related_works":["https://openalex.org/W3166133680","https://openalex.org/W2104703456","https://openalex.org/W2884768918","https://openalex.org/W2735859191","https://openalex.org/W4289753429","https://openalex.org/W50519898","https://openalex.org/W2592341669","https://openalex.org/W2376290921","https://openalex.org/W2987631717","https://openalex.org/W2097892801"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3],"traffic":[4,30,71],"flow":[5,72],"estimation":[6],"method":[7],"for":[8],"communication":[9],"networks":[10],"using":[11,113],"higher":[12],"order":[13],"Markov":[14,54],"chain":[15],"and":[16,25,97,123],"Incremental":[17],"Gaussian":[18,103],"Mixture":[19,104],"Model":[20,105],"(IGMM).":[21],"Given":[22],"the":[23,32,36,45,50,57,81],"previous":[24],"current":[26],"values":[27,73],"of":[28,53,85],"network":[29],"flow,":[31],"optimal":[33],"prediction":[34],"under":[35],"minimum":[37],"mean":[38],"square":[39],"error":[40],"criteria":[41],"is":[42,60,76,125],"given":[43],"as":[44,70],"conditional":[46],"expectation":[47],"according":[48],"to":[49,78,102],"transition":[51,58],"probability":[52,59,82],"chain.":[55],"Since":[56],"not":[61],"known":[62],"beforehand,":[63],"IGMM,":[64],"whose":[65],"mixtures":[66],"are":[67],"updated":[68],"on-line":[69,90],"become":[74],"known,":[75],"used":[77],"instantaneously":[79],"change":[80],"density":[83],"function":[84],"mixtures.":[86],"IGMM":[87,120],"with":[88],"an":[89],"learning":[91],"mechanism":[92],"has":[93],"lower":[94,99],"computational":[95],"complexity":[96],"requires":[98],"memory":[100],"compared":[101],"(GMM)":[106],"which":[107],"uses":[108],"batch":[109],"processing.":[110],"Numerical":[111],"experiments":[112],"publicly":[114],"available":[115],"\u201cAbilene\u201d":[116],"data-set":[117],"show":[118],"that":[119],"outperforms":[121],"GMM,":[122],"it":[124],"more":[126],"robust.":[127]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}
