{"id":"https://openalex.org/W2799272162","doi":"https://doi.org/10.1137/1.9781611975321.19","title":"Online Truth Discovery on Time Series Data","display_name":"Online Truth Discovery on Time Series Data","publication_year":2018,"publication_date":"2018-05-07","ids":{"openalex":"https://openalex.org/W2799272162","doi":"https://doi.org/10.1137/1.9781611975321.19","mag":"2799272162"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611975321.19","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611975321.19","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 SIAM International Conference on Data Mining","raw_type":"book-chapter"},"type":"book-chapter","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/A5032616889","display_name":"Liuyi Yao","orcid":"https://orcid.org/0000-0003-3828-796X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liuyi Yao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732938","display_name":"L\u00fc Su","orcid":"https://orcid.org/0000-0001-7223-543X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu Su","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350205","display_name":"Qi Li","orcid":"https://orcid.org/0000-0002-3136-2157"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046576694","display_name":"Yaliang Li","orcid":"https://orcid.org/0000-0002-4204-6096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaliang Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001030192","display_name":"Fenglong Ma","orcid":"https://orcid.org/0000-0002-4999-0303"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fenglong Ma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781389","display_name":"Jing Gao","orcid":"https://orcid.org/0000-0002-7139-1227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013588572","display_name":"Aidong Zhang","orcid":"https://orcid.org/0000-0001-9723-3246"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aidong Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.23,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.95915679,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"162","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9973999857902527,"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":0.9973999857902527,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9969000220298767,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/computer-science","display_name":"Computer science","score":0.772762656211853},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.554737389087677},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5488024353981018},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5306813716888428},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49572718143463135},{"id":"https://openalex.org/keywords/business-process-discovery","display_name":"Business process discovery","score":0.4459816813468933},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4336383044719696},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.4172278940677643},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4113750457763672},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.32521408796310425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2903294563293457},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2895292043685913},{"id":"https://openalex.org/keywords/work-in-process","display_name":"Work in process","score":0.14756116271018982}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.772762656211853},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.554737389087677},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5488024353981018},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5306813716888428},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49572718143463135},{"id":"https://openalex.org/C93453677","wikidata":"https://www.wikidata.org/wiki/Q1017580","display_name":"Business process discovery","level":5,"score":0.4459816813468933},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4336383044719696},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.4172278940677643},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4113750457763672},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32521408796310425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2903294563293457},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2895292043685913},{"id":"https://openalex.org/C174998907","wikidata":"https://www.wikidata.org/wiki/Q357662","display_name":"Work in process","level":2,"score":0.14756116271018982},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C207505557","wikidata":"https://www.wikidata.org/wiki/Q4374012","display_name":"Business process modeling","level":4,"score":0.0},{"id":"https://openalex.org/C85345410","wikidata":"https://www.wikidata.org/wiki/Q851587","display_name":"Business process","level":3,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/1.9781611975321.19","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611975321.19","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 SIAM International Conference on Data Mining","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320318547","display_name":"Baidu","ror":"https://ror.org/03vs3wt56"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1541280084","https://openalex.org/W1565102206","https://openalex.org/W1667830255","https://openalex.org/W1713409046","https://openalex.org/W1728753295","https://openalex.org/W1945395050","https://openalex.org/W1992766323","https://openalex.org/W2013976210","https://openalex.org/W2034771068","https://openalex.org/W2048716023","https://openalex.org/W2054379763","https://openalex.org/W2055733113","https://openalex.org/W2094634352","https://openalex.org/W2103872473","https://openalex.org/W2107254606","https://openalex.org/W2119363954","https://openalex.org/W2126007324","https://openalex.org/W2131222034","https://openalex.org/W2140394893","https://openalex.org/W2151073469","https://openalex.org/W2155189155","https://openalex.org/W2159296364","https://openalex.org/W2160220160","https://openalex.org/W2162237605","https://openalex.org/W2313953460","https://openalex.org/W2531361673","https://openalex.org/W2751717865","https://openalex.org/W2798056406","https://openalex.org/W2798766386","https://openalex.org/W3146166473"],"related_works":["https://openalex.org/W1989373239","https://openalex.org/W57948323","https://openalex.org/W2143920642","https://openalex.org/W2060045119","https://openalex.org/W2496603770","https://openalex.org/W118909908","https://openalex.org/W2103281268","https://openalex.org/W2336675426","https://openalex.org/W2185491808","https://openalex.org/W110081222"],"abstract_inverted_index":{"Truth":[0],"discovery,":[1],"with":[2],"the":[3,14,40,44,62,103,106,111,118,122,126,130,135,138,145,152],"goal":[4],"of":[5,43,125,137,151],"inferring":[6],"true":[7],"information":[8,15,120],"from":[9,16],"massive":[10],"data":[11,18,45,63,109],"through":[12],"aggregating":[13],"multiple":[17],"sources,":[19],"has":[20,28],"attracted":[21],"significant":[22],"attention":[23],"in":[24,32,50,66],"recent":[25],"years.":[26],"It":[27],"demonstrated":[29],"great":[30],"advantages":[31],"real":[33],"applications":[34],"since":[35],"it":[36],"can":[37,83,133],"automatically":[38],"learn":[39],"reliability":[41],"degrees":[42],"sources":[46],"without":[47],"supervision":[48],"and":[49,69,121,160],"turn":[51],"helps":[52],"to":[53],"find":[54],"more":[55],"reliable":[56],"information.":[57],"In":[58],"many":[59],"applications,":[60],"however,":[61],"may":[64],"arrive":[65],"a":[67,95],"stream":[68],"present":[70],"various":[71],"temporal":[72,123],"patterns.":[73],"Unfortunately,":[74],"there":[75],"is":[76,155],"no":[77],"existing":[78],"truth":[79,98,112,139],"discovery":[80,99,140],"work":[81],"that":[82,101],"handle":[84],"such":[85],"time":[86,107,127,146],"series":[87,108,128,147],"data.":[88],"To":[89],"tackle":[90],"this":[91],"challenge,":[92],"we":[93],"propose":[94],"novel":[96],"online":[97],"framework":[100,132,154],"incorporates":[102],"predictions":[104],"on":[105,157],"into":[110],"estimation":[113],"process.":[114],"By":[115],"jointly":[116],"considering":[117],"multi-source":[119],"patterns":[124],"data,":[129],"proposed":[131,153],"improve":[134],"accuracy":[136],"results":[141],"as":[142,144],"well":[143],"prediction.":[148],"The":[149],"effectiveness":[150],"validated":[156],"both":[158],"synthetic":[159],"real-world":[161],"datasets.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
