{"id":"https://openalex.org/W4391094162","doi":"https://doi.org/10.1109/bigdata59044.2023.10386554","title":"Exploring the Performance Impacts of Training Predictive Models with Inclusive Email Threads","display_name":"Exploring the Performance Impacts of Training Predictive Models with Inclusive Email Threads","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391094162","doi":"https://doi.org/10.1109/bigdata59044.2023.10386554"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386554","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386554","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/A5002671147","display_name":"Chris Clark","orcid":"https://orcid.org/0000-0001-9982-7849"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chris Clark","raw_affiliation_strings":["Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA"],"affiliations":[{"raw_affiliation_string":"Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101046778","display_name":"Han Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han Qin","raw_affiliation_strings":["Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA"],"affiliations":[{"raw_affiliation_string":"Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049186333","display_name":"Nathaniel Huber-Fliflet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nathaniel Huber-Fliflet","raw_affiliation_strings":["Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA"],"affiliations":[{"raw_affiliation_string":"Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101619458","display_name":"A. D\u0105browski","orcid":"https://orcid.org/0009-0004-5689-2500"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adam Dabrowski","raw_affiliation_strings":["Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA"],"affiliations":[{"raw_affiliation_string":"Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076180260","display_name":"Jianping Zhang","orcid":"https://orcid.org/0000-0003-2212-5296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianping Zhang","raw_affiliation_strings":["Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA"],"affiliations":[{"raw_affiliation_string":"Data &#x0026; Technology Ankura Consulting Group LLC,Washington,D.C. USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5002671147"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2134864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"2755","last_page":"2760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9961000084877014,"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"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9961000084877014,"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"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9940000176429749,"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":0.7380959391593933},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5735874176025391},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.32903602719306946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7380959391593933},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5735874176025391},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.32903602719306946},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"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":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386554","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386554","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2049201511","https://openalex.org/W2399815506","https://openalex.org/W2413512986","https://openalex.org/W2585689263","https://openalex.org/W2784310254","https://openalex.org/W3013534122","https://openalex.org/W4318147781","https://openalex.org/W6715324707","https://openalex.org/W6763932988"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W230091440","https://openalex.org/W2390279801","https://openalex.org/W2233261550","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2810751659"],"abstract_inverted_index":{"Email":[0],"threading":[1,169,181,248,317],"is":[2,90,109,176,182,204,216],"a":[3,31,110,116,173,198,202,222,255,265,280,322],"commonly":[4],"used":[5,113,217],"tool":[6,112],"by":[7,252],"legal":[8,17,64,97,153,239,306,330],"practitioners":[9,66,307],"to":[10,28,58,69,77,114,137,188,207,218,225,308],"streamline":[11,192],"document":[12,40,83,118,140,193,325],"review":[13,84,119,309],"and":[14,37,44,60,73,85,120,146,179,191,212,220,260,269,277,318],"classification":[15,121,326],"in":[16,95],"proceedings.":[18,331],"Threading":[19],"organizes":[20],"component":[21],"pieces":[22],"of":[23,152,162,229,246,298,314],"an":[24,167],"email":[25,124,168,180,210,247,275,288,316],"thread":[26,48],"together":[27],"effectively":[29],"reduce":[30,79,190],"dataset.":[32],"The":[33,296],"most":[34],"inclusive":[35,209,274,287],"threads":[36],"their":[38,329],"associated":[39,148],"attachments":[41],"are":[42,50],"maintained,":[43],"non-inclusive":[45],"or":[46,80,102],"duplicative":[47],"components":[49],"set":[51],"aside.":[52],"However,":[53],"as":[54,99],"data":[55,235],"volumes":[56],"continue":[57],"grow":[59],"outpace":[61],"deadlines":[62],"for":[63,133,231,305,328],"proceedings,":[65,240],"often":[67],"look":[68],"incorporate":[70],"multiple":[71],"cost-effective":[72],"defensible":[74],"technology":[75,89],"solutions":[76],"further":[78,189],"otherwise":[81],"accelerate":[82],"classification.":[86,232],"One":[87],"such":[88],"predictive":[91,126,164,174,223,250,319],"modeling":[92,165,251,320],"\u2013":[93,107],"known":[94],"the":[96,144,159,185,226,243,312],"industry":[98],"\u2018predictive":[100],"coding\u2019":[101],"\u2018Technology":[103],"Assisted":[104],"Review":[105],"(TAR)\u2019":[106],"which":[108],"popular":[111],"augment":[115],"manual":[117,139],"process.":[122],"Like":[123],"threading,":[125],"coding":[127],"has":[128],"become":[129],"more":[130],"commonplace":[131],"recently":[132],"its":[134,208],"proven":[135],"ability":[136],"minimize":[138],"classification,":[141],"thus":[142],"reducing":[143],"time":[145],"cost":[147],"with":[149],"this":[150,155,213],"aspect":[151],"proceedings.In":[154],"study,":[156],"we":[157,241],"explore":[158],"performance":[160,244],"impacts":[161],"layering":[163],"onto":[166,184],"reduction":[170],"workflow.":[171],"Generally,":[172],"model":[175,224,256,266,281],"established":[177],"first,":[178],"layered":[183],"scored":[186],"output":[187],"review.":[194],"Our":[195],"research":[196,300],"evaluates":[197],"reversed":[199],"workflow,":[200],"where":[201],"population":[203,228],"initially":[205],"reduced":[206],"threads,":[211,276,289],"limited":[214],"dataset":[215],"train":[219],"apply":[221],"larger":[227],"documents":[230],"Using":[233],"classified":[234],"from":[236,272,285,293],"four":[237],"real-world":[238],"compare":[242],"impact":[245],"on":[249],"1)":[253],"training":[254,264,279],"using":[257,267,282],"all":[258,294],"positive":[259,268,283],"negative":[261,270,291],"examples,":[262],"2)":[263],"examples":[271,284,292],"only":[273,286],"3)":[278],"but":[290],"emails.":[295],"results":[297],"our":[299],"provide":[301],"thoughtful,":[302],"empirical":[303],"insights":[304],"when":[310],"exploring":[311],"deployment":[313],"both":[315],"into":[321],"single,":[323],"cohesive":[324],"strategy":[327]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
