{"id":"https://openalex.org/W2968796649","doi":"https://doi.org/10.1109/cscwd.2019.8791861","title":"Selecting Superior Candidates from a Suitable Set: A Selective Extraction Algorithm for Accelerating Shapelet Discovery in Time Series Data","display_name":"Selecting Superior Candidates from a Suitable Set: A Selective Extraction Algorithm for Accelerating Shapelet Discovery in Time Series Data","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2968796649","doi":"https://doi.org/10.1109/cscwd.2019.8791861","mag":"2968796649"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd.2019.8791861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd.2019.8791861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102822149","display_name":"Chao Zhao","orcid":"https://orcid.org/0000-0002-3367-0268"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Zhao","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002025125","display_name":"Shijun Liu","orcid":"https://orcid.org/0000-0002-4108-1391"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijun Liu","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037159442","display_name":"Li Pan","orcid":"https://orcid.org/0000-0001-6157-3740"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Pan","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066754863","display_name":"Cun Ji","orcid":"https://orcid.org/0000-0002-3326-6572"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cun Ji","raw_affiliation_strings":["School of Information Science & Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101863852","display_name":"Chenglei Yang","orcid":"https://orcid.org/0000-0002-9353-8218"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglei Yang","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"online first","issue":null,"first_page":"404","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994000196456909,"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":0.9994000196456909,"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/T12114","display_name":"Sensory Analysis and Statistical Methods","score":0.9466000199317932,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10750","display_name":"Fermentation and Sensory Analysis","score":0.9390000104904175,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7263630628585815},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5572418570518494},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4918341636657715},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.47309306263923645},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4620721936225891},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42298054695129395},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4199536442756653},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33319273591041565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3152950406074524},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.16304224729537964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7263630628585815},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5572418570518494},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4918341636657715},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.47309306263923645},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4620721936225891},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42298054695129395},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4199536442756653},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33319273591041565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3152950406074524},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.16304224729537964},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/cscwd.2019.8791861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd.2019.8791861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1978371851","https://openalex.org/W1984674851","https://openalex.org/W1994200512","https://openalex.org/W2011208599","https://openalex.org/W2027096757","https://openalex.org/W2029438113","https://openalex.org/W2123502857","https://openalex.org/W2152542198","https://openalex.org/W2163487757","https://openalex.org/W2308479694","https://openalex.org/W2402972623","https://openalex.org/W2437679399","https://openalex.org/W2519731719","https://openalex.org/W2555077524","https://openalex.org/W2572265523","https://openalex.org/W2584499795","https://openalex.org/W2607072734","https://openalex.org/W2702877955","https://openalex.org/W2803239750","https://openalex.org/W6648659803","https://openalex.org/W6682529325","https://openalex.org/W6718456096","https://openalex.org/W6730050545","https://openalex.org/W6731925191","https://openalex.org/W6732978115","https://openalex.org/W6739563127"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3046370962"],"abstract_inverted_index":{"A":[0],"serious":[1],"challenge":[2],"that":[3,166],"confronts":[4],"shapelet-based":[5],"algorithms":[6,21],"for":[7],"time":[8,44,82,89,140,180],"series":[9,83,90,141],"classification":[10],"is":[11,39,101,182],"finding":[12],"optimal":[13,150],"shapelets":[14,23,151],"in":[15,59,113,117,173],"a":[16,64,81],"short":[17],"time.":[18],"Representative":[19],"shapelet-discovery":[20],"find":[22],"by":[24],"evaluating":[25],"the":[26,32,36,40,48,56,72,120,129,132,138,157,167,179],"qualities":[27],"of":[28,35,43,51,74,131],"candidates":[29,109,122,134],"extracted":[30],"from":[31,152],"subsequences.":[33],"One":[34],"main":[37],"difficulties":[38],"large":[41],"amount":[42],"consumed,":[45],"due":[46],"to":[47,70,87,103,148],"excessive":[49],"number":[50],"shapelet":[52,75,105,121,133],"candidates.":[53,106,153],"To":[54,127],"address":[55],"above":[57],"problem,":[58],"this":[60],"paper":[61],"we":[62,136],"propose":[63],"fast":[65],"and":[66,115,142],"interpretable":[67],"candidate-extraction":[68],"algorithm":[69,79,159,169],"accelerate":[71],"process":[73],"discovery.":[76],"The":[77,107,163],"proposed":[78,158,168],"utilizes":[80],"subclass":[84],"splitting":[85],"technique":[86,147],"sample":[88],"dataset":[91],"first.":[92],"Then,":[93],"an":[94,144],"IDP":[95],"(Important":[96],"Data":[97],"Point)-based":[98],"selective-extraction":[99],"strategy":[100],"used":[102],"extract":[104],"generated":[108,123],"have":[110],"significant":[111,171],"improvements":[112,172],"quality":[114],"reductions":[116],"quantity.":[118],"Furthermore,":[119],"are":[124],"more":[125],"interpretable.":[126],"test":[128],"effectiveness":[130],"generated,":[135],"transform":[137],"original":[139],"use":[143],"off-the-shelf":[145],"attribute-selection":[146],"select":[149],"We":[154],"then":[155],"evaluate":[156],"through":[160],"extensive":[161],"experiments.":[162],"results":[164],"demonstrate":[165],"makes":[170],"accuracy,":[174],"compared":[175],"with":[176],"baselines.":[177],"Meanwhile,":[178],"consumption":[181],"also":[183],"greatly":[184],"reduced.":[185]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
