{"id":"https://openalex.org/W2140914274","doi":"https://doi.org/10.1145/1774088.1774436","title":"Online mining of temporal maximal utility itemsets from data streams","display_name":"Online mining of temporal maximal utility itemsets from data streams","publication_year":2010,"publication_date":"2010-03-22","ids":{"openalex":"https://openalex.org/W2140914274","doi":"https://doi.org/10.1145/1774088.1774436","mag":"2140914274"},"language":"en","primary_location":{"id":"doi:10.1145/1774088.1774436","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","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/A5017342073","display_name":"Bai En Shie","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Bai-En Shie","raw_affiliation_strings":["National Cheng Kung University, Taiwan, ROC","National Cheng Kung University , Taiwan, ROC"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University, Taiwan, ROC","institution_ids":["https://openalex.org/I91807558"]},{"raw_affiliation_string":"National Cheng Kung University , Taiwan, ROC","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043399804","display_name":"Vincent S. Tseng","orcid":"https://orcid.org/0000-0002-4853-1594"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Vincent S. Tseng","raw_affiliation_strings":["National Cheng Kung University, Taiwan, ROC","National Cheng Kung University , Taiwan, ROC"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University, Taiwan, ROC","institution_ids":["https://openalex.org/I91807558"]},{"raw_affiliation_string":"National Cheng Kung University , Taiwan, ROC","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, Illinois"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, Illinois","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017342073"],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":8.578,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.97552996,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1622","last_page":"1626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9947999715805054,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/data-stream-mining","display_name":"Data stream mining","score":0.8039251565933228},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.8026542067527771},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7806406021118164},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.6277134418487549},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5214018821716309},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4346208870410919},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.412354052066803},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09734055399894714}],"concepts":[{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.8039251565933228},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.8026542067527771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7806406021118164},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.6277134418487549},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5214018821716309},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4346208870410919},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.412354052066803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09734055399894714},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1774088.1774436","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.721.5796","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.5796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://idb.csie.ncku.edu.tw/paper/conference/Online%20Mining%20of%20Temporal%20Maximal%20Utility%20Itemsets%20from%20Data%20Streams.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5580402289","display_name":null,"funder_award_id":"NSC 96-2221-E-006-143-MY3","funder_id":"https://openalex.org/F4320321040","funder_display_name":"National Science Council"},{"id":"https://openalex.org/G8716734295","display_name":null,"funder_award_id":"98-2631-H-006-001","funder_id":"https://openalex.org/F4320321040","funder_display_name":"National Science Council"}],"funders":[{"id":"https://openalex.org/F4320321040","display_name":"National Science Council","ror":"https://ror.org/02kv4zf79"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W110175884","https://openalex.org/W1484413656","https://openalex.org/W1506285740","https://openalex.org/W1569347009","https://openalex.org/W2064853889","https://openalex.org/W2084318750","https://openalex.org/W2103946246","https://openalex.org/W2107586730","https://openalex.org/W2107934556","https://openalex.org/W2114423174","https://openalex.org/W2114469075","https://openalex.org/W2115274736","https://openalex.org/W2119939946","https://openalex.org/W2123426165","https://openalex.org/W2132851185","https://openalex.org/W2138101451","https://openalex.org/W2138660495","https://openalex.org/W2151201497","https://openalex.org/W4252403066"],"related_works":["https://openalex.org/W2738041616","https://openalex.org/W1888905147","https://openalex.org/W2545008743","https://openalex.org/W2128573033","https://openalex.org/W3008753261","https://openalex.org/W2355681927","https://openalex.org/W1966256265","https://openalex.org/W2086364952","https://openalex.org/W1646313322","https://openalex.org/W2492122152"],"abstract_inverted_index":{"Data":[0,81],"stream":[1,24,172],"mining":[2,12,25,31,136,164],"has":[3],"become":[4],"an":[5,19],"emerging":[6],"research":[7],"topic":[8],"in":[9,22,51],"the":[10,41,55,61,110,130,170],"data":[11,23,91,95,142,171],"field,":[13],"and":[14,144,149],"finding":[15],"frequent":[16,56],"itemsets":[17,57,89,140],"is":[18,32,104,129,147],"important":[20],"task":[21],"with":[26,36,115],"wide":[27],"applications.":[28],"Recently,":[29],"utility":[30,42,88,111,139,163],"receiving":[33],"extensive":[34],"attentions":[35],"two":[37],"issues":[38],"reconsidered:":[39],"First,":[40],"(e.g.,":[43],"profit)":[44],"of":[45,75,112,121],"each":[46,113],"item":[47],"may":[48],"be":[49],"different":[50],"real":[52],"applications;":[53],"second,":[54],"might":[58],"not":[59],"produce":[60],"highest":[62],"utility.":[63],"In":[64],"this":[65,122],"paper,":[66],"we":[67],"propose":[68],"a":[69],"novel":[70,94],"algorithm":[71,134,168],"named":[72],"GUIDE":[73,128],"(Generation":[74],"temporal":[76,86,137],"maximal":[77,87,138],"Utility":[78,101],"Itemsets":[79],"from":[80,90,141],"strEams)":[82],"which":[83],"can":[84],"find":[85],"streams.":[92],"A":[93],"structure,":[96],"namely,":[97],"TMUI-tree":[98,146],"(Temporal":[99],"Maximal":[100],"Itemset":[102],"tree),":[103],"also":[105],"proposed":[106],"for":[107,135],"efficiently":[108],"capturing":[109],"itemset":[114],"one-time":[116],"scanning.":[117],"The":[118,153],"main":[119],"contributions":[120],"paper":[123],"are":[124],"as":[125],"follows:":[126],"1)":[127],"first":[131],"one-pass":[132],"utility-based":[133],"streams,":[143],"2)":[145],"efficient":[148],"easy":[150],"to":[151],"maintain.":[152],"experimental":[154],"results":[155],"show":[156],"that":[157],"our":[158],"approach":[159],"outperforms":[160],"other":[161],"existing":[162],"algorithms":[165],"like":[166],"Two-Phase":[167],"under":[169],"environments.":[173]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":13},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
