{"id":"https://openalex.org/W4391097767","doi":"https://doi.org/10.1109/bigdata59044.2023.10386289","title":"Causal Associations between Temporal Events","display_name":"Causal Associations between Temporal Events","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391097767","doi":"https://doi.org/10.1109/bigdata59044.2023.10386289"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386289","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","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/A5102755635","display_name":"Nguyen Ho","orcid":"https://orcid.org/0000-0002-2308-4329"},"institutions":[{"id":"https://openalex.org/I165556055","display_name":"Loyola University Maryland","ror":"https://ror.org/01by1wp65","country_code":"US","type":"education","lineage":["https://openalex.org/I165556055"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nguyen Ho","raw_affiliation_strings":["Loyola University,Department of Computer Science,Maryland,USA","Department of Computer Science, Loyola University, Maryland, USA"],"affiliations":[{"raw_affiliation_string":"Loyola University,Department of Computer Science,Maryland,USA","institution_ids":["https://openalex.org/I165556055"]},{"raw_affiliation_string":"Department of Computer Science, Loyola University, Maryland, USA","institution_ids":["https://openalex.org/I165556055"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114120474","display_name":"Trinh Cong Le","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137358","display_name":"Quy Nhon University","ror":"https://ror.org/04h4y6q56","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210137358"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Trinh Cong Le","raw_affiliation_strings":["Quy Nhon University,Department of Mathematics,Vietnam","Department of Mathematics, Quy Nhon University, Vietnam"],"affiliations":[{"raw_affiliation_string":"Quy Nhon University,Department of Mathematics,Vietnam","institution_ids":["https://openalex.org/I4210137358"]},{"raw_affiliation_string":"Department of Mathematics, Quy Nhon University, Vietnam","institution_ids":["https://openalex.org/I4210137358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033219727","display_name":"Van Long Ho","orcid":"https://orcid.org/0000-0002-7673-7186"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Van Long Ho","raw_affiliation_strings":["Aalborg University,Department of Computer Science,Denmark","Department of Computer Science, Aalborg University, Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University,Department of Computer Science,Denmark","institution_ids":["https://openalex.org/I891191580"]},{"raw_affiliation_string":"Department of Computer Science, Aalborg University, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102340519","display_name":"Nguy\u00ean T\u01b0\u1eddng Hu\u1ef3nh","orcid":null},"institutions":[{"id":"https://openalex.org/I202539009","display_name":"Industrial University of Ho Chi Minh City","ror":"https://ror.org/03mj71j26","country_code":"VN","type":"education","lineage":["https://openalex.org/I202539009"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Nguyen Tuong Huynh","raw_affiliation_strings":["Industrial University of Ho Chi Minh City,Department of Computer Science,Vietnam","Department of Computer Science, Industrial University of Ho Chi Minh City, Vietnam"],"affiliations":[{"raw_affiliation_string":"Industrial University of Ho Chi Minh City,Department of Computer Science,Vietnam","institution_ids":["https://openalex.org/I202539009"]},{"raw_affiliation_string":"Department of Computer Science, Industrial University of Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I202539009"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022485068","display_name":"Linh Ngo Van","orcid":"https://orcid.org/0000-0002-0011-5137"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]},{"id":"https://openalex.org/I161171246","display_name":"West Chester University","ror":"https://ror.org/0053n5071","country_code":"US","type":"education","lineage":["https://openalex.org/I161171246"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Linh Ngo","raw_affiliation_strings":["West Chester University of Pennsylvania,Department of Computer Science,USA","Department of Computer Science, West Chester University of Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"West Chester University of Pennsylvania,Department of Computer Science,USA","institution_ids":["https://openalex.org/I161171246","https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Computer Science, West Chester University of Pennsylvania, USA","institution_ids":["https://openalex.org/I161171246","https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102755635"],"corresponding_institution_ids":["https://openalex.org/I165556055"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19353007,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":null,"first_page":"1135","last_page":"1142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9987000226974487,"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":0.9987000226974487,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9797000288963318,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9642999768257141,"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/causal-inference","display_name":"Causal inference","score":0.7313850522041321},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.7277154922485352},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.675794243812561},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6678059697151184},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6188852787017822},{"id":"https://openalex.org/keywords/duration","display_name":"Duration (music)","score":0.5691219568252563},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5662682056427002},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.5516112446784973},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.5359123349189758},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4762834906578064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4735245704650879},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45447593927383423},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.44291675090789795},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43931859731674194},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.380146861076355},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27606895565986633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17914286255836487}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7313850522041321},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7277154922485352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675794243812561},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6678059697151184},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6188852787017822},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.5691219568252563},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5662682056427002},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.5516112446784973},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5359123349189758},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4762834906578064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4735245704650879},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45447593927383423},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.44291675090789795},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43931859731674194},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.380146861076355},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27606895565986633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17914286255836487},{"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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386289","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/45c686e2-75e7-4dc1-a37d-3b05a7fe4e69","is_oa":false,"landing_page_url":"https://vbn.aau.dk/da/publications/45c686e2-75e7-4dc1-a37d-3b05a7fe4e69","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ho, N, Le, T C, Ho, V L, Huynh, N T & Ngo, L 2023, Causal Associations between Temporal Events. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, J C-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. IEEE (Institute of Electrical and Electronics Engineers), pp. 1135-1142, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/2023. https://doi.org/10.1109/BigData59044.2023.10386289","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1501652345","https://openalex.org/W2009271606","https://openalex.org/W2127214298","https://openalex.org/W2135631431","https://openalex.org/W2178225550","https://openalex.org/W2382897425","https://openalex.org/W2594127743","https://openalex.org/W2754880706","https://openalex.org/W2817024182","https://openalex.org/W2892226239","https://openalex.org/W2949505632","https://openalex.org/W2996833126","https://openalex.org/W3034644316","https://openalex.org/W3081699158","https://openalex.org/W3214860080","https://openalex.org/W4205365706","https://openalex.org/W6601179954","https://openalex.org/W6680271190","https://openalex.org/W6709678119","https://openalex.org/W6713360694","https://openalex.org/W6722373760","https://openalex.org/W6722660299","https://openalex.org/W6754949890"],"related_works":["https://openalex.org/W2348837382","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W2574301230","https://openalex.org/W1547624382","https://openalex.org/W4320159092"],"abstract_inverted_index":{"Causal":[0],"inference":[1,25,56],"from":[2,26,36],"observational":[3,37],"data":[4],"has":[5,29],"been":[6],"widely":[7],"studied":[8],"to":[9,18,68,162],"infer":[10],"causal":[11,24,55,74,139],"relations":[12],"between":[13,141],"causes":[14],"and":[15,46,64,95,120,124,154,167],"effects.":[16],"Due":[17],"the":[19,44,61,73,77,117,125,159,171,183,190],"popularity":[20],"of":[21,43,49,109],"event-based":[22,50],"data,":[23],"event":[27,38,62,97,135,156],"datasets":[28,153,157],"attracted":[30],"increasing":[31],"interest.":[32],"However,":[33],"inferring":[34,138],"causalities":[35],"sequences":[39],"is":[40,65],"challenging":[41],"because":[42],"heterogeneous":[45],"irregular":[47],"nature":[48],"data.":[51],"Existing":[52],"work":[53],"on":[54,72,100],"for":[57,90],"temporal":[58,91,103,142],"events":[59,92],"disregards":[60],"durations,":[63],"thus":[66],"unable":[67],"capture":[69],"their":[70],"impact":[71],"relations.":[75],"In":[76],"present":[78],"paper,":[79],"we":[80,105],"overcome":[81],"this":[82,101],"limitation":[83],"by":[84],"proposing":[85],"a":[86,107,186],"new":[87,102],"modeling":[88],"approach":[89],"that":[93,131,178],"captures":[94],"utilizes":[96],"durations.":[98],"Based":[99],"model,":[104],"propose":[106],"set":[108],"novel":[110],"Duration-based":[111,118,126],"Event":[112],"Causality":[113],"(DEC)":[114],"scores,":[115,166],"including":[116],"Necessity":[119],"Sufficiency":[121],"Trade-off":[122],"score,":[123],"Conditional":[127],"Intensity":[128],"Rates":[129],"scores":[130,181],"take":[132],"into":[133],"consideration":[134],"durations":[136],"when":[137],"associations":[140],"events.":[143],"We":[144],"conduct":[145],"an":[146],"extensive":[147],"experimental":[148,175],"evaluation":[149,192],"using":[150,189],"both":[151],"synthetic":[152],"real-world":[155],"in":[158],"environmental":[160],"domains":[161],"evaluate":[163],"our":[164,179],"proposed":[165,180],"compare":[168],"them":[169],"against":[170],"closest":[172],"baseline.":[173],"The":[174],"results":[176],"show":[177],"outperform":[182],"baseline":[184],"with":[185],"large":[187],"margin":[188],"popular":[191],"metric":[193],"Hits@K.":[194]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}