{"id":"https://openalex.org/W2811044682","doi":"https://doi.org/10.1109/fskd.2017.8393066","title":"A multidimensional time-series association rules algorithm based on spark","display_name":"A multidimensional time-series association rules algorithm based on spark","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2811044682","doi":"https://doi.org/10.1109/fskd.2017.8393066","mag":"2811044682"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5089586803","display_name":"Dongyue Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"DongYue Liu","raw_affiliation_strings":["Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043114890","display_name":"Bin Wu","orcid":"https://orcid.org/0000-0001-5787-9536"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wu","raw_affiliation_strings":["Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046975123","display_name":"Chao Gu","orcid":"https://orcid.org/0000-0002-4234-9871"},"institutions":[{"id":"https://openalex.org/I1335486098","display_name":"Electric Power Research Institute","ror":"https://ror.org/02dqztz06","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1335486098"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Gu","raw_affiliation_strings":["State Grid Shandong Electric Power Research Institute"],"affiliations":[{"raw_affiliation_string":"State Grid Shandong Electric Power Research Institute","institution_ids":["https://openalex.org/I1335486098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102862727","display_name":"Yan Ma","orcid":"https://orcid.org/0009-0000-2525-0055"},"institutions":[{"id":"https://openalex.org/I1335486098","display_name":"Electric Power Research Institute","ror":"https://ror.org/02dqztz06","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1335486098"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Ma","raw_affiliation_strings":["State Grid Shandong Electric Power Research Institute"],"affiliations":[{"raw_affiliation_string":"State Grid Shandong Electric Power Research Institute","institution_ids":["https://openalex.org/I1335486098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100608154","display_name":"Bai Wang","orcid":"https://orcid.org/0009-0007-4592-795X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bai Wang","raw_affiliation_strings":["Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, PR China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5089586803"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.4836,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.77895728,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1946","last_page":"1952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9980999827384949,"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":0.9980999827384949,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9891999959945679,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9842000007629395,"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/series","display_name":"Series (stratigraphy)","score":0.6821880340576172},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.6445703506469727},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.63800048828125},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.6266705393791199},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5228748321533203},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.469840943813324},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3184545338153839}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6821880340576172},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.6445703506469727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.63800048828125},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.6266705393791199},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5228748321533203},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.469840943813324},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3184545338153839},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1607960813","https://openalex.org/W2025754122","https://openalex.org/W2075628943","https://openalex.org/W2083308546","https://openalex.org/W2086605334","https://openalex.org/W2100809207","https://openalex.org/W2111248848","https://openalex.org/W2119313249","https://openalex.org/W2147745637","https://openalex.org/W2155238168","https://openalex.org/W2158454296","https://openalex.org/W2189465200","https://openalex.org/W2282737348","https://openalex.org/W2319067044","https://openalex.org/W2327069718","https://openalex.org/W2353098811","https://openalex.org/W2388718221","https://openalex.org/W2517850251","https://openalex.org/W2528709811","https://openalex.org/W2542550679","https://openalex.org/W6636223253","https://openalex.org/W6687322159","https://openalex.org/W6726337007"],"related_works":["https://openalex.org/W2392697706","https://openalex.org/W366033468","https://openalex.org/W128746893","https://openalex.org/W2367573304","https://openalex.org/W2537030075","https://openalex.org/W2006971496","https://openalex.org/W2065998343","https://openalex.org/W2369717039","https://openalex.org/W2384676159","https://openalex.org/W2982449560"],"abstract_inverted_index":{"Fault":[0],"prediction":[1,57],"of":[2,37,50,58,95,105,116,159,174,197],"industrial":[3,59,79],"systems":[4,80],"has":[5],"been":[6,98],"a":[7,127],"hot":[8],"research":[9],"orientation":[10],"in":[11,29,55,78,135,186],"recent":[12],"years,":[13],"which":[14,138,169],"allows":[15],"the":[16,20,24,35,40,51,93,106,110,114,117,123,142,157,160,164,172,175,182,192,195,198],"maintainer":[17],"to":[18,26,33,91,121,190],"know":[19],"operation":[21],"conditions":[22],"and":[23,39,112,144,194],"fault":[25,38,56],"be":[27],"occurred":[28],"advance":[30],"so":[31],"as":[32],"reduce":[34,92,113],"risk":[36],"economic":[41],"loss.":[42],"In":[43,119],"general,":[44],"association":[45,66],"rules":[46,67,108,149],"learning":[47],"is":[48],"one":[49],"most":[52],"effective":[53],"methods":[54,63,87,101],"systems,":[60],"however,":[61],"traditional":[62],"based":[64,88,162],"on":[65,89,163,181],"are":[68,76,179],"not":[69],"suitable":[70],"for":[71],"sparse":[72],"time-series":[73,152],"data":[74,96],"that":[75],"common":[77],"(e.g.":[81],"transmission":[82,183],"line":[83,184],"data).":[84],"Although":[85],"some":[86,104],"clustering":[90],"dimension":[94],"have":[97],"proposed,":[99],"these":[100],"may":[102],"lose":[103],"key":[107],"from":[109,150],"dataset":[111,185],"effectiveness":[115,193],"results.":[118],"order":[120],"solve":[122],"problem,":[124],"we":[125,155],"propose":[126],"novel":[128],"algorithm":[129,161,176],"called":[130],"Multidimensional":[131],"Time-series":[132],"Association":[133],"Rules(MTAR)":[134],"this":[136],"paper,":[137],"can":[139,170],"fully":[140],"utilize":[141],"information":[143],"find":[145],"out":[146],"more":[147],"valuable":[148],"multidimensional":[151],"data.":[153],"Meanwhile,":[154],"implement":[156],"parallelization":[158],"parallel":[165],"computing":[166],"framework":[167],"Spark,":[168],"improve":[171],"performance":[173],"greatly.":[177],"Experiments":[178],"conducted":[180],"Power":[187],"Grid":[188],"System":[189],"show":[191],"efficiency":[196],"proposed":[199],"approach.":[200]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}