{"id":"https://openalex.org/W7118122453","doi":"https://doi.org/10.1186/s40537-025-01352-x","title":"Intelligent average utility pattern analysis using pre-large concept in dynamic stream data","display_name":"Intelligent average utility pattern analysis using pre-large concept in dynamic stream data","publication_year":2026,"publication_date":"2026-01-04","ids":{"openalex":"https://openalex.org/W7118122453","doi":"https://doi.org/10.1186/s40537-025-01352-x"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01352-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01352-x","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s40537-025-01352-x","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100427660","display_name":"Do\u2010Young Kim","orcid":"https://orcid.org/0000-0003-4682-5545"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Doyoung Kim","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121904310","display_name":"Seongbin Park","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongbin Park","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121887855","display_name":"Junyoung Park","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junyoung Park","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121901010","display_name":"Hanju Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hanju Kim","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113235926","display_name":"Seungwan Park","orcid":"https://orcid.org/0009-0003-7753-8079"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungwan Park","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086377572","display_name":"Myungha Cho","orcid":"https://orcid.org/0009-0005-3551-4292"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Myungha Cho","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079834335","display_name":"Unil Yun","orcid":"https://orcid.org/0000-0002-3720-0861"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Unil Yun","raw_affiliation_strings":["Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5079834335"],"corresponding_institution_ids":["https://openalex.org/I28777354"],"apc_list":{"value":1990,"currency":"USD","value_usd":1990},"apc_paid":{"value":1990,"currency":"USD","value_usd":1990},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03333665,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.8927000164985657,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.8927000164985657,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.031700000166893005,"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"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.020999999716877937,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6812000274658203},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5723000168800354},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.527999997138977},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.47690001130104065},{"id":"https://openalex.org/keywords/pattern-analysis","display_name":"Pattern analysis","score":0.46140000224113464},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.43470001220703125},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.41019999980926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8431000113487244},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6812000274658203},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6312000155448914},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.527999997138977},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C2985264313","wikidata":"https://www.wikidata.org/wiki/Q378859","display_name":"Pattern analysis","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C82691427","wikidata":"https://www.wikidata.org/wiki/Q4291856","display_name":"Pattern search","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3314000070095062},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C14501506","wikidata":"https://www.wikidata.org/wiki/Q5253831","display_name":"Design pattern","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C2989134064","wikidata":"https://www.wikidata.org/wiki/Q288510","display_name":"Execution time","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01352-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01352-x","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:062431341379410f9d95347197ff6d69","is_oa":true,"landing_page_url":"https://doaj.org/article/062431341379410f9d95347197ff6d69","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 13, Iss 1, Pp 1-34 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01352-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01352-x","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1966121832","https://openalex.org/W2010528952","https://openalex.org/W2028087213","https://openalex.org/W2143428105","https://openalex.org/W2161637667","https://openalex.org/W2340848227","https://openalex.org/W2552617282","https://openalex.org/W2587969590","https://openalex.org/W2604029285","https://openalex.org/W2795516748","https://openalex.org/W2796882849","https://openalex.org/W2909756225","https://openalex.org/W2976436416","https://openalex.org/W3015232492","https://openalex.org/W3022695007","https://openalex.org/W3038220444","https://openalex.org/W3042648958","https://openalex.org/W3084369642","https://openalex.org/W3119260152","https://openalex.org/W3154256082","https://openalex.org/W3160736894","https://openalex.org/W3173485164","https://openalex.org/W3197161527","https://openalex.org/W3199517509","https://openalex.org/W3211405969","https://openalex.org/W4387541693","https://openalex.org/W4387789485","https://openalex.org/W4389945043","https://openalex.org/W4399110533","https://openalex.org/W4403777719","https://openalex.org/W4404022116","https://openalex.org/W4406615007","https://openalex.org/W4407693112","https://openalex.org/W4407957904","https://openalex.org/W4408611773","https://openalex.org/W4408941159","https://openalex.org/W4409077140","https://openalex.org/W4410344873","https://openalex.org/W4410543547","https://openalex.org/W4414008486","https://openalex.org/W4414291841"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"studies":[1],"on":[2,139],"high":[3,75],"average":[4,76,132],"utility":[5,77,133],"pattern":[6,17,30,58,105,115],"analysis":[7],"aim":[8],"to":[9,54,151,200],"extract":[10],"patterns":[11,50,78],"from":[12],"quantitative":[13],"data":[14,37],"considering":[15],"the":[16,26,29,80,83,87,92,111,114,131,146,167,172,176,185,201],"length":[18],"in":[19],"incremental":[20,36],"environments.":[21],"However,":[22],"traditional":[23],"methods":[24,122,154],"have":[25],"limitation":[27],"that":[28,45,145,171],"expansion":[31,59,116],"process":[32,117],"is":[33,42,71,149],"conducted":[34],"whenever":[35],"occur.":[38],"The":[39],"pre-large":[40,84],"concept":[41,85,189],"a":[43,62,68,96],"technique":[44],"addresses":[46],"this":[47,66],"by":[48],"classifying":[49],"and":[51,57,123,141,157,184],"leveraging":[52,79],"them":[53],"reduce":[55],"re-scan":[56,63,89,100],"operations":[60],"with":[61,86,103,162],"condition.":[64,90],"In":[65],"paper,":[67],"novel":[69],"approach":[70,94,109,174],"proposed":[72,93,98,147,173],"for":[73],"analyzing":[74],"framework":[81],"of":[82,113,134],"tight":[88,99],"Specifically,":[91],"adopts":[95],"newly":[97],"condition":[101],"combined":[102],"effective":[104],"tree":[106],"management.":[107],"This":[108],"manages":[110],"occurrence":[112],"more":[118],"efficiently":[119],"than":[120],"state-of-the-art":[121,202],"reduces":[124],"redundant":[125],"computations":[126],"while":[127,159],"extracting":[128],"results":[129],"through":[130],"each":[135],"pattern.":[136],"Comprehensive":[137],"experiments":[138],"real":[140],"synthetic":[142],"datasets":[143],"demonstrate":[144],"method":[148],"superior":[150,194],"other":[152],"comparison":[153],"regarding":[155],"runtime":[156,179],"scalability":[158],"exhibiting":[160],"completeness":[161],"competitive":[163],"memory":[164],"usage.":[165],"Furthermore,":[166],"sensitivity":[168],"tests":[169,191],"indicate":[170],"maintains":[175],"most":[177],"stable":[178],"performance":[180],"under":[181,188],"varying":[182],"thresholds,":[183],"case":[186],"study":[187],"drift":[190],"demonstrates":[192],"its":[193,197],"scalability,":[195],"showing":[196],"applicability":[198],"compared":[199],"approach.":[203]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-01-04T00:00:00"}
