{"id":"https://openalex.org/W4403577759","doi":"https://doi.org/10.1145/3627673.3680083","title":"COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data","display_name":"COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577759","doi":"https://doi.org/10.1145/3627673.3680083"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3680083","doi":"https://doi.org/10.1145/3627673.3680083","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3680083","pdf_url":null,"source":{"id":"https://openalex.org/S4393918199","display_name":"ACM International Conference on Information and Knowledge Management (CIKM)","issn_l":"2155-0751","issn":["2155-0751"],"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":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2407.12254","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115604858","display_name":"T. Ou","orcid":"https://orcid.org/0009-0008-7277-1759","observed_orcids":["https://orcid.org/0009-0008-7277-1759"]},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ting-Yun Ou","raw_affiliation_strings":["Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0009-0008-7277-1759","affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112982678","display_name":"Ching Chang","orcid":"https://orcid.org/0000-0002-5031-5487","observed_orcids":["https://orcid.org/0000-0002-5031-5487"]},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ching Chang","raw_affiliation_strings":["Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5031-5487","affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040101293","display_name":"Wen-Chih Peng","orcid":"https://orcid.org/0000-0002-0172-7311","observed_orcids":["https://orcid.org/0000-0002-0172-7311","https://orcid.org/0000-0001-8964-0933"]},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Chih Peng","raw_affiliation_strings":["Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-0172-7311","affiliations":[{"raw_affiliation_string":"Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":0.7893,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.7671404,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4803","last_page":"4810"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.6693999767303467,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.6693999767303467,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.2556000053882599,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causal-discovery","display_name":"causal discovery","score":0.996999979019165},{"id":"https://openalex.org/keywords/missing-data","display_name":"missing data","score":0.7129999995231628},{"id":"https://openalex.org/keywords/production-data","display_name":"production data","score":0.6330000162124634},{"id":"https://openalex.org/keywords/expert-knowledge","display_name":"expert knowledge","score":0.8989999890327454},{"id":"https://openalex.org/keywords/chronological-order","display_name":"chronological order","score":0.9449999928474426},{"id":"https://openalex.org/keywords/causal-diagrams","display_name":"causal graphs","score":0.6480000019073486},{"id":"https://openalex.org/keywords/actor-critic","display_name":"actor-critic","score":0.6809999942779541},{"id":"https://openalex.org/keywords/graph-generation","display_name":"graph generation","score":0.5379999876022339},{"id":"https://openalex.org/keywords/semiconductor-manufacturing","display_name":"semiconductor manufacturing","score":0.7009999752044678},{"id":"https://openalex.org/keywords/fault-diagnosis","display_name":"fault diagnosis","score":0.777999997138977},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.652999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5191099643707275},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.49025896191596985},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.48133668303489685},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.47920331358909607},{"id":"https://openalex.org/C2780385392","wikidata":"https://www.wikidata.org/wiki/Q192795","display_name":"Coke","level":2,"score":0.44020792841911316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39353638887405396},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38122138381004333},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22124487161636353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2005457580089569},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12877479195594788},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3627673.3680083","doi":"https://doi.org/10.1145/3627673.3680083","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3680083","pdf_url":null,"source":{"id":"https://openalex.org/S4393918199","display_name":"ACM International Conference on Information and Knowledge Management (CIKM)","issn_l":"2155-0751","issn":["2155-0751"],"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":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2407.12254","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.12254","pdf_url":"https://arxiv.org/pdf/2407.12254","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2407.12254","doi":"https://doi.org/10.48550/arxiv.2407.12254","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2407.12254","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2407.12254","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.12254","pdf_url":"https://arxiv.org/pdf/2407.12254","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403577759.pdf","grobid_xml":"https://content.openalex.org/works/W4403577759.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2021556180","https://openalex.org/W2031341655","https://openalex.org/W2064186732","https://openalex.org/W2067578371","https://openalex.org/W2086435977","https://openalex.org/W2094971008","https://openalex.org/W2100358124","https://openalex.org/W2151226328","https://openalex.org/W2291203944","https://openalex.org/W2396961413","https://openalex.org/W2478894641","https://openalex.org/W3035215110","https://openalex.org/W3041187887","https://openalex.org/W3139004781","https://openalex.org/W3187238269","https://openalex.org/W4223978494","https://openalex.org/W4281387928","https://openalex.org/W4365397963","https://openalex.org/W4387713111","https://openalex.org/W4393161123"],"related_works":["https://openalex.org/W7130405506","https://openalex.org/W4281387928","https://openalex.org/W4405787498","https://openalex.org/W4312796233","https://openalex.org/W4385838135","https://openalex.org/W6901875885","https://openalex.org/W4312507998","https://openalex.org/W3092109552","https://openalex.org/W7018711396","https://openalex.org/W2168935573"],"abstract_inverted_index":{"Understanding":[0],"causal":[1,54,103],"relationships":[2,55],"between":[3],"machines":[4],"is":[5,50,230],"crucial":[6],"for":[7,52,63],"fault":[8],"diagnosis":[9],"and":[10,24,38,112,148,151,185,221],"optimization":[11],"in":[12,67,105,179,203,223],"manufacturing":[13,58,106],"processes.":[14],"Real-world":[15],"datasets":[16,32,92,107],"frequently":[17],"exhibit":[18,94],"up":[19],"to":[20,70,77,101,167,218],"90%":[21],"missing":[22,65,95,119,134,186],"data":[23,66],"high":[25],"dimensionality":[26],"from":[27,138],"hundreds":[28],"of":[29,124,131,182,201],"sensors.":[30],"These":[31],"also":[33],"include":[34],"domain-specific":[35],"expert":[36,80,88,110,146],"knowledge":[37,89,111,147],"chronological":[39,113,149],"order":[40,45,114],"information,":[41],"reflecting":[42],"the":[43,57,122,125,129,194,204,207,215],"recording":[44],"across":[46],"different":[47],"machines,":[48],"which":[49],"pivotal":[51],"discerning":[53],"within":[56],"data.":[59,120],"However,":[60],"previous":[61],"methods":[62,84,196],"handling":[64],"scenarios":[68],"akin":[69],"real-world":[71,219,224],"conditions":[72],"have":[73],"not":[74],"been":[75,161],"able":[76],"effectively":[78],"utilize":[79],"knowledge.":[81],"Conversely,":[82],"prior":[83],"that":[85,93,144,172,189],"can":[86,210],"incorporate":[87],"struggle":[90],"with":[91,133,140,193],"values.":[96],"Therefore,":[97],"we":[98,127],"propose":[99],"COKE":[100],"construct":[102],"graphs":[104],"by":[108,163],"leveraging":[109],"among":[115],"sensors":[116],"without":[117],"imputing":[118],"Utilizing":[121],"characteristics":[123],"recipe,":[126],"maximize":[128],"use":[130],"samples":[132],"values,":[135],"derive":[136],"embeddings":[137],"intersections":[139],"an":[141,164,198],"initial":[142],"graph":[143,171],"incorporates":[145],"order,":[150],"create":[152],"a":[153,169,174],"sensor":[154,183],"ordering":[155],"graph.":[156],"The":[157,227],"graph-generating":[158],"process":[159],"has":[160,173],"optimized":[162],"actor-critic":[165],"architecture":[166],"obtain":[168],"final":[170],"maximum":[175],"reward.":[176],"Experimental":[177],"evaluations":[178],"diverse":[180],"settings":[181],"quantities":[184],"proportions":[187],"demonstrate":[188],"our":[190],"approach":[191],"compared":[192],"benchmark":[195],"shows":[197],"average":[199],"improvement":[200,209],"39.9%":[202],"F1-score.":[205],"Moreover,":[206],"F1-score":[208],"reach":[211],"62.6%":[212],"when":[213],"considering":[214],"configuration":[216],"similar":[217],"datasets,":[220],"85.0%":[222],"semiconductor":[225],"datasets.":[226],"source":[228],"code":[229],"available":[231],"at":[232],"https://github.com/OuTingYun/COKE.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2024-10-20T00:00:00"}