{"id":"https://openalex.org/W3086971674","doi":"https://doi.org/10.1109/inista49547.2020.9194656","title":"Speeding up the SUCCESS Approach for Massive Industrial Datasets","display_name":"Speeding up the SUCCESS Approach for Massive Industrial Datasets","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3086971674","doi":"https://doi.org/10.1109/inista49547.2020.9194656","mag":"3086971674"},"language":"en","primary_location":{"id":"doi:10.1109/inista49547.2020.9194656","doi":"https://doi.org/10.1109/inista49547.2020.9194656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/inista49547.2020.9194656","pdf_url":null,"source":{"id":"https://openalex.org/S4306498847","display_name":"International Conference on Innovations in Intelligent Systems and Applications (INISTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5032448573","display_name":"Kriszti\u00e1n B\u00faza","orcid":"https://orcid.org/0000-0002-7111-6452","observed_orcids":["https://orcid.org/0000-0002-7111-6452"]},"institutions":[{"id":"https://openalex.org/I106118109","display_name":"E\u00f6tv\u00f6s Lor\u00e1nd University","ror":"https://ror.org/01jsq2704","country_code":"HU","type":"education","lineage":["https://openalex.org/I106118109"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Krisztian Buza","raw_affiliation_strings":["Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University (ELTE), Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University (ELTE), Budapest, Hungary","institution_ids":["https://openalex.org/I106118109"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059202921","display_name":"Aleksandra Revina","orcid":"https://orcid.org/0000-0002-8405-0018","observed_orcids":["https://orcid.org/0000-0002-8405-0018"]},"institutions":[{"id":"https://openalex.org/I4210146756","display_name":"Brandenburg University of Applied Sciences","ror":"https://ror.org/04qj3gf68","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210146756"]},{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Aleksandra Revina","raw_affiliation_strings":["Faculty of Economics, Brandenburg University of Applied Sciences Brandenburg an der Havel, Germany Faculty of Economics and Management, Technical University of Berlin, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, Brandenburg University of Applied Sciences Brandenburg an der Havel, Germany Faculty of Economics and Management, Technical University of Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4210146756","https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.657,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6548486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9707000255584717,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9707000255584717,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.017100000753998756,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0010000000474974513,"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/success","display_name":"success","score":0.6140000224113464},{"id":"https://openalex.org/keywords/semi-supervised-classification","display_name":"semi-supervised classification","score":0.878000020980835},{"id":"https://openalex.org/keywords/time-series-classification","display_name":"time series classification","score":0.9959999918937683},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.7250000238418579},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5289999842643738},{"id":"https://openalex.org/keywords/industrial-data","display_name":"industrial data","score":0.6190000176429749},{"id":"https://openalex.org/keywords/large-scale-data","display_name":"large-scale data","score":0.5220000147819519}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7968218326568604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6666057109832764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5705984234809875},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4802732467651367},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4743046760559082},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4721662700176239},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4686260521411896},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.45316237211227417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1332516372203827},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/inista49547.2020.9194656","doi":"https://doi.org/10.1109/inista49547.2020.9194656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/inista49547.2020.9194656","pdf_url":null,"source":{"id":"https://openalex.org/S4306498847","display_name":"International Conference on Innovations in Intelligent Systems and Applications (INISTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","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":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W96651161","https://openalex.org/W113907902","https://openalex.org/W135909296","https://openalex.org/W168772536","https://openalex.org/W1516407653","https://openalex.org/W1557517461","https://openalex.org/W1580625695","https://openalex.org/W1589643523","https://openalex.org/W1620389387","https://openalex.org/W1790904062","https://openalex.org/W2079057609","https://openalex.org/W2085789144","https://openalex.org/W2101210369","https://openalex.org/W2106401878","https://openalex.org/W2127017215","https://openalex.org/W2136504847","https://openalex.org/W2138776269","https://openalex.org/W2140234018","https://openalex.org/W2147741500","https://openalex.org/W2184783769","https://openalex.org/W2249231905","https://openalex.org/W2267568327","https://openalex.org/W2280718384","https://openalex.org/W2462697095","https://openalex.org/W2744652752","https://openalex.org/W2790266980","https://openalex.org/W2798360154","https://openalex.org/W2904753829","https://openalex.org/W2953153648","https://openalex.org/W2984353870","https://openalex.org/W2998496101","https://openalex.org/W3003341045","https://openalex.org/W3023534255","https://openalex.org/W3192000169"],"related_works":["https://openalex.org/W7097311795","https://openalex.org/W7100816958","https://openalex.org/W2499228648","https://openalex.org/W7095604501","https://openalex.org/W2982676708","https://openalex.org/W2744652752","https://openalex.org/W3183074967","https://openalex.org/W2232010405","https://openalex.org/W4284975766","https://openalex.org/W2154031574"],"abstract_inverted_index":{"In":[0,73],"many":[1],"applications,":[2],"it":[3],"may":[4,26],"be":[5,28],"expensive,":[6],"difficult":[7],"or":[8],"even":[9],"impossible":[10],"to":[11,33,63,78,89],"obtain":[12],"class":[13],"labels":[14],"for":[15,50],"a":[16,84],"large":[17],"amount":[18],"of":[19,53,95,106,118],"the":[20,23,47,100,104,116,121,127],"instances,":[21],"therefore,":[22],"labeled":[24,40],"data":[25],"not":[27,69],"representative.":[29],"Semi-supervised":[30],"learning":[31],"aims":[32],"alleviate":[34],"this":[35,74,80],"problem":[36],"by":[37],"using":[38],"both":[39,107,123],"and":[41,110,126],"unlabeled":[42],"data.":[43],"Recently,":[44],"we":[45,76,82,114],"introduced":[46],"SUCCESS":[48,57,92],"approach":[49,102],"semi-supervised":[51],"classification":[52,105],"time":[54],"series.":[55],"Although":[56],"achieved":[58],"promising":[59],"results,":[60],"its":[61,96],"ability":[62],"classify":[64],"massive,":[65],"industrial":[66,111],"datasets":[67],"has":[68],"been":[70],"studied":[71],"yet.":[72],"paper,":[75],"aim":[77],"fill":[79],"gap:":[81],"propose":[83],"simple":[85],"but":[86],"effective":[87],"method":[88],"speed":[90],"up":[91],"without":[93],"loss":[94],"accuracy.":[97],"We":[98],"evaluate":[99],"resulting":[101],"on":[103],"publicly":[108],"available":[109],"datasets.":[112],"Hence,":[113],"expect":[115],"increase":[117],"interest":[119],"in":[120,124],"algorithm":[122],"industry":[125],"research":[128],"community.":[129]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-09-21T00:00:00"}
