{"id":"https://openalex.org/W2968518053","doi":"https://doi.org/10.1109/tnsm.2019.2934133","title":"Multivariate Multi-Order Markov Multi-Modal Prediction With Its Applications in Network Traffic Management","display_name":"Multivariate Multi-Order Markov Multi-Modal Prediction With Its Applications in Network Traffic Management","publication_year":2019,"publication_date":"2019-08-09","ids":{"openalex":"https://openalex.org/W2968518053","doi":"https://doi.org/10.1109/tnsm.2019.2934133","mag":"2968518053"},"language":"en","primary_location":{"id":"doi:10.1109/tnsm.2019.2934133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2019.2934133","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-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/A5034601329","display_name":"Huazhong Liu","orcid":"https://orcid.org/0000-0001-7813-5894"},"institutions":[{"id":"https://openalex.org/I134626604","display_name":"Jiujiang University","ror":"https://ror.org/0066vpg85","country_code":"CN","type":"education","lineage":["https://openalex.org/I134626604"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huazhong Liu","raw_affiliation_strings":["School of Information Science and Technology, Jiujiang University, Jiujiang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Jiujiang University, Jiujiang, China","institution_ids":["https://openalex.org/I134626604"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049154222","display_name":"Laurence T. Yang","orcid":"https://orcid.org/0000-0002-7986-4244"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Laurence T. Yang","raw_affiliation_strings":["School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101808353","display_name":"Jinjun Chen","orcid":"https://orcid.org/0000-0003-1677-9525"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jinjun Chen","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068195523","display_name":"Minghao Ye","orcid":"https://orcid.org/0000-0003-0173-6127"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minghao Ye","raw_affiliation_strings":["Department of Electrical and Computer Engineering, New York University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086641506","display_name":"Jihong Ding","orcid":"https://orcid.org/0000-0002-2939-6171"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihong Ding","raw_affiliation_strings":["School of Education Science and Technology, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Education Science and Technology, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018138106","display_name":"Liwei Kuang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155232","display_name":"Fiberhome Technology Group (China)","ror":"https://ror.org/04yv20134","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155232"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwei Kuang","raw_affiliation_strings":["Network Business Unit, FiberHome Telecommunication Technologies Company Ltd., Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Network Business Unit, FiberHome Telecommunication Technologies Company Ltd., Wuhan, China","institution_ids":["https://openalex.org/I4210155232"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5034601329"],"corresponding_institution_ids":["https://openalex.org/I134626604"],"apc_list":null,"apc_paid":null,"fwci":3.5972,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.94360902,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"16","issue":"3","first_page":"828","last_page":"841"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9929999709129333,"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/T10138","display_name":"Network Traffic and Congestion Control","score":0.9804999828338623,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.722465455532074},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.6538853049278259},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.5247547626495361},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5138931274414062},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5135743618011475},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4872371256351471},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.48125144839286804},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4553428888320923},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.4254996180534363},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36258891224861145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2901758551597595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28306013345718384},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.22339525818824768},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16871806979179382},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.10658144950866699},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08788013458251953}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722465455532074},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6538853049278259},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5247547626495361},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5138931274414062},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5135743618011475},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4872371256351471},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.48125144839286804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4553428888320923},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.4254996180534363},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36258891224861145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2901758551597595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28306013345718384},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.22339525818824768},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16871806979179382},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.10658144950866699},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08788013458251953},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnsm.2019.2934133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2019.2934133","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"},{"id":"pmh:oai:researchbank.swinburne.edu.au:ae695f76-a740-4139-ab08-54dae621ff90/1","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.3/451721","pdf_url":null,"source":{"id":"https://openalex.org/S4306401157","display_name":"Swinburne Research Bank (Swinburne University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I57093077","host_organization_name":"Swinburne University of Technology","host_organization_lineage":["https://openalex.org/I57093077"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Network and Service Management, Vol. 16, no. 3 (Sep 2019), pp. 828-841","raw_type":""}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5571805579","display_name":null,"funder_award_id":"71764036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6211856567","display_name":null,"funder_award_id":"71704160","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7773080550","display_name":null,"funder_award_id":"61867002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1509244992","https://openalex.org/W1523742540","https://openalex.org/W1652644688","https://openalex.org/W1803102843","https://openalex.org/W1965608158","https://openalex.org/W1965857701","https://openalex.org/W1988356170","https://openalex.org/W2006331477","https://openalex.org/W2024165284","https://openalex.org/W2027419845","https://openalex.org/W2031545685","https://openalex.org/W2040403168","https://openalex.org/W2048636460","https://openalex.org/W2051000513","https://openalex.org/W2067261106","https://openalex.org/W2073478238","https://openalex.org/W2089194229","https://openalex.org/W2090208105","https://openalex.org/W2124168655","https://openalex.org/W2148506461","https://openalex.org/W2280094791","https://openalex.org/W2312974704","https://openalex.org/W2324823555","https://openalex.org/W2514754763","https://openalex.org/W2516041031","https://openalex.org/W2564479228","https://openalex.org/W2604930203","https://openalex.org/W2745993090","https://openalex.org/W2762414772","https://openalex.org/W2766252098","https://openalex.org/W2785984019","https://openalex.org/W2791492153","https://openalex.org/W2809684781","https://openalex.org/W2888994561","https://openalex.org/W2901174000","https://openalex.org/W3001110585","https://openalex.org/W3037346199","https://openalex.org/W3105184776","https://openalex.org/W6772846366"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582","https://openalex.org/W2082284720","https://openalex.org/W2116722627","https://openalex.org/W2537260108","https://openalex.org/W2158700654","https://openalex.org/W2379938888","https://openalex.org/W2566202039","https://openalex.org/W4233405330"],"abstract_inverted_index":{"Predicting":[0],"the":[1,14,35,47,51,122,135,173,180,195],"future":[2],"network":[3,18,164,184],"traffic":[4,165,185],"through":[5],"big":[6,38],"data":[7],"analysis":[8],"technologies":[9],"has":[10,30],"been":[11],"one":[12],"of":[13,17,37,53,156],"important":[15],"preoccupations":[16],"design":[19],"and":[20,55,90,146],"management.":[21],"Combining":[22],"Markov":[23,45,72,100,118,160],"chains":[24],"with":[25,43,102,194],"tensors":[26],"to":[27,74,120,133,189],"implement":[28,121],"predictions":[29],"received":[31],"considerable":[32],"attention":[33],"in":[34],"era":[36],"data.":[39],"However,":[40],"when":[41],"dealing":[42],"multi-order":[44,71,98],"models,":[46],"existing":[48],"approaches":[49],"including":[50,87],"combination":[52],"states":[54],"Z-eigen":[56,196],"decomposition":[57],"still":[58],"face":[59],"some":[60],"shortcomings.":[61],"Therefore,":[62],"this":[63],"paper":[64],"focuses":[65],"on":[66,162],"proposing":[67],"a":[68,95,112,154],"novel":[69],"multivariate":[70,97],"transition":[73,106,114],"realize":[75,147],"multi-modal":[76,150],"accurate":[77,151],"predictions.":[78,152],"First,":[79],"we":[80,110],"put":[81],"forward":[82],"two":[83],"new":[84],"tensor":[85,88,115,140],"operations":[86],"join":[89],"unified":[91],"product":[92],"(UP).":[93],"Then":[94],"general":[96],"(2M)":[99],"model":[101],"its":[103],"UP-based":[104,128],"state":[105,124],"is":[107,131],"proposed.":[108],"Afterwards,":[109],"develop":[111],"multi-step":[113,123],"for":[116,183],"2M":[117],"models":[119,161],"transition.":[125],"Furthermore,":[126],"an":[127],"power":[129],"method":[130],"proposed":[132,174],"calculate":[134],"stationary":[136,142],"joint":[137,143],"probability":[138],"distribution":[139],"(i.e.,":[141],"eigentensor,":[144],"SJE)":[145],"SJE":[148,175],"based":[149,176,197],"Finally,":[153],"series":[155],"experiments":[157],"under":[158],"various":[159],"real-world":[163],"datasets":[166],"are":[167],"conducted.":[168],"Experimental":[169],"results":[170],"demonstrate":[171],"that":[172],"approach":[177],"can":[178],"improve":[179],"prediction":[181],"accuracy":[182],"by":[186],"highest":[187],"up":[188],"38.47":[190],"percentage":[191],"points":[192],"compared":[193],"approach.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}