{"id":"https://openalex.org/W4413680008","doi":"https://doi.org/10.23919/wiopt66569.2025.11123319","title":"Trading Fresh Data with Correlation","display_name":"Trading Fresh Data with Correlation","publication_year":2025,"publication_date":"2025-05-26","ids":{"openalex":"https://openalex.org/W4413680008","doi":"https://doi.org/10.23919/wiopt66569.2025.11123319"},"language":"en","primary_location":{"id":"doi:10.23919/wiopt66569.2025.11123319","is_oa":false,"landing_page_url":"https://doi.org/10.23919/wiopt66569.2025.11123319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 23rd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","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/A5101723460","display_name":"Junyi He","orcid":"https://orcid.org/0000-0001-7795-9357"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyi He","raw_affiliation_strings":["Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,Guangdong,China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100437745","display_name":"Meng Zhang","orcid":"https://orcid.org/0000-0002-4893-6946"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]},{"id":"https://openalex.org/I4210158570","display_name":"Zhejiang University-University of Edinburgh Institute","ror":"https://ror.org/04jth1r26","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210158570","https://openalex.org/I76130692","https://openalex.org/I98677209"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Meng Zhang","raw_affiliation_strings":["Zhejiang University/University of Illinois at Urbana Champaign Institute, Zhejiang University,Haining,China,314400"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University/University of Illinois at Urbana Champaign Institute, Zhejiang University,Haining,China,314400","institution_ids":["https://openalex.org/I157725225","https://openalex.org/I4210158570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103248914","display_name":"Qian Ma","orcid":"https://orcid.org/0000-0002-8672-6069"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Ma","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-sen University,Shenzhen,Guangdong,China,518107"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-sen University,Shenzhen,Guangdong,China,518107","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062346297","display_name":"Jianwei Huang","orcid":"https://orcid.org/0000-0001-6631-1096"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Huang","raw_affiliation_strings":["School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong,Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network, and CSIJRI Joint Research Centre on Smart Energy Storage,Shenzhen,Guangdong,P.R. China,518172"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong,Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network, and CSIJRI Joint Research Centre on Smart Energy Storage,Shenzhen,Guangdong,P.R. China,518172","institution_ids":["https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.3431,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.93005181,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.6633999943733215,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.6633999943733215,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.6223000288009644,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4974866211414337},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4265602231025696},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1544228196144104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4974866211414337},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4265602231025696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1544228196144104},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/wiopt66569.2025.11123319","is_oa":false,"landing_page_url":"https://doi.org/10.23919/wiopt66569.2025.11123319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 23rd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2994774842","display_name":null,"funder_award_id":"No. 62271434 and No. 62372481","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1994005439","https://openalex.org/W2744248483","https://openalex.org/W2797442898","https://openalex.org/W2801262348","https://openalex.org/W2889679132","https://openalex.org/W2891280833","https://openalex.org/W2964345816","https://openalex.org/W3019050693","https://openalex.org/W3107210262","https://openalex.org/W3113239917","https://openalex.org/W3128630999","https://openalex.org/W3134599018","https://openalex.org/W3137257456","https://openalex.org/W3139018907","https://openalex.org/W3153624705","https://openalex.org/W3154774595","https://openalex.org/W3201587594","https://openalex.org/W4287119468","https://openalex.org/W4297882647","https://openalex.org/W4320170194","https://openalex.org/W4386260502","https://openalex.org/W4390098238","https://openalex.org/W4392523529","https://openalex.org/W4400351448","https://openalex.org/W4402350729","https://openalex.org/W4402978208","https://openalex.org/W4406138043","https://openalex.org/W4413680008"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0],"increasing":[1],"reliance":[2],"on":[3,169],"fresh":[4,14,38],"data":[5,23,51,61,78,118,134,144,155,179,196,221,237],"in":[6,26,93,177,194],"real-time":[7,68],"applications":[8,27],"underscores":[9],"the":[10,21,34,44,63,67,88,149,172,202,215,225],"significance":[11],"of":[12,36,48,70,74,174,214],"commoditized":[13],"data.":[15,161],"However,":[16],"current":[17],"research":[18],"often":[19],"neglects":[20],"crucial":[22],"correlation,":[24],"essential":[25],"like":[28],"intelligent":[29],"transportation.":[30],"This":[31,183],"paper":[32],"examines":[33],"trading":[35],"correlated":[37,50,159],"data,":[39,171],"where":[40],"a":[41,71,82,128,166],"platform":[42,64,89,150],"monitors":[43],"time-varying":[45],"numerical":[46],"status":[47,69],"multiple":[49],"sources.":[52],"Data":[53],"users":[54,152,164,191],"arrive":[55],"stochastically,":[56],"each":[57],"seeking":[58],"to":[59,65,90,124,189,192,230,234],"obtain":[60],"from":[62],"estimate":[66],"specific":[72],"source":[73],"interest.":[75],"To":[76],"facilitate":[77],"trading,":[79],"we":[80,136],"propose":[81,109],"dynamic":[83,99,205,217],"pricing":[84,100,185,206,218],"policy":[85,207],"that":[86,98,143,201],"allows":[87],"adjust":[91],"prices":[92],"real":[94],"time.":[95],"We":[96],"demonstrate":[97],"is":[101,187],"an":[102,110,138],"NP-hard":[103],"mixed":[104],"integer":[105],"programming":[106,123],"problem":[107],"and":[108,120,151],"approximate":[111,204],"algorithm.":[112],"Our":[113],"approach":[114],"begins":[115],"with":[116,157],"threshold-based":[117],"allocation":[119],"uses":[121],"linear":[122],"optimize":[125],"pricing,":[126],"achieving":[127],"logarithmic":[129],"approximation":[130],"ratio.":[131],"For":[132],"binary":[133],"sources,":[135],"derive":[137],"optimal":[139],"closed-form":[140],"solution,":[141],"revealing":[142],"correlation":[145,175,222],"can":[146,208,223],"benefit":[147],"both":[148],"by":[153,228],"offsetting":[154],"aging":[156],"spatially":[158],"fresher":[160,170,178],"Interestingly,":[162],"despite":[163],"placing":[165],"higher":[167],"valuation":[168],"presence":[173],"results":[176,199],"being":[180],"priced":[181],"lower.":[182],"counterintuitive":[184],"strategy":[186],"designed":[188],"encourage":[190],"engage":[193],"crosssource":[195],"purchasing.":[197],"Numerical":[198],"show":[200],"proposed":[203],"achieve":[209],"at":[210],"least":[211],"90":[212],"%":[213,232],"maximum":[216],"revenue.":[219],"Additionally,":[220],"amplify":[224],"platform's":[226],"revenue":[227],"up":[229],"100":[231],"compared":[233],"scenarios":[235],"without":[236],"correlation.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}