{"id":"https://openalex.org/W4399174677","doi":"https://doi.org/10.1145/3654946","title":"High Precision \u2260 High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors","display_name":"High Precision \u2260 High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors","publication_year":2024,"publication_date":"2024-05-29","ids":{"openalex":"https://openalex.org/W4399174677","doi":"https://doi.org/10.1145/3654946"},"language":"en","primary_location":{"id":"doi:10.1145/3654946","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654946","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-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/A5101599455","display_name":"Jingyu Zhu","orcid":"https://orcid.org/0009-0009-1611-7717"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Zhu","raw_affiliation_strings":["College of Computer Science, Nankai university, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0009-1611-7717","affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai university, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734399","display_name":"Yu Sun","orcid":"https://orcid.org/0009-0007-7398-2972"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Sun","raw_affiliation_strings":["College of Computer Science, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0007-7398-2972","affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084430029","display_name":"Shaoxu Song","orcid":"https://orcid.org/0000-0002-9503-2755"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoxu Song","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9503-2755","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062064974","display_name":"Xiaojie Yuan","orcid":"https://orcid.org/0000-0002-5876-6856"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Yuan","raw_affiliation_strings":["College of Computer Science, Nankai Univeristy, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5876-6856","affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai Univeristy, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6109,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72003772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"2","issue":"3","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9980999827384949,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9980999827384949,"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":0.9897000193595886,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9868000149726868,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.7585053443908691},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7237493395805359},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.626604437828064},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.6171505451202393},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5523081421852112},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5498745441436768},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.534083366394043},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5062707662582397},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.43325936794281006},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4312199652194977},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3307008445262909},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27801477909088135},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09232518076896667}],"concepts":[{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.7585053443908691},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7237493395805359},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.626604437828064},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6171505451202393},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5523081421852112},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5498745441436768},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.534083366394043},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5062707662582397},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.43325936794281006},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4312199652194977},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3307008445262909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27801477909088135},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09232518076896667},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3654946","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654946","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2154614518","display_name":"\u65f6\u5e8f\u6570\u636e\u6e05\u7406\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"62072265","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4671081833","display_name":null,"funder_award_id":"22JCQNJC01520","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G8194792314","display_name":null,"funder_award_id":"62302241","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1541769218","https://openalex.org/W1567491469","https://openalex.org/W1568654892","https://openalex.org/W1661441792","https://openalex.org/W1667830255","https://openalex.org/W1673310716","https://openalex.org/W1749494163","https://openalex.org/W1916324727","https://openalex.org/W1917948361","https://openalex.org/W1968010112","https://openalex.org/W1969483458","https://openalex.org/W1971329220","https://openalex.org/W1981202432","https://openalex.org/W2000237244","https://openalex.org/W2011833091","https://openalex.org/W2013976210","https://openalex.org/W2020196977","https://openalex.org/W2020934227","https://openalex.org/W2026682405","https://openalex.org/W2032790727","https://openalex.org/W2036557187","https://openalex.org/W2042961981","https://openalex.org/W2050877865","https://openalex.org/W2053134894","https://openalex.org/W2073545563","https://openalex.org/W2078280078","https://openalex.org/W2086413055","https://openalex.org/W2094634352","https://openalex.org/W2105934661","https://openalex.org/W2106191211","https://openalex.org/W2117013146","https://openalex.org/W2124941493","https://openalex.org/W2127218421","https://openalex.org/W2127825015","https://openalex.org/W2130887504","https://openalex.org/W2149433885","https://openalex.org/W2153344006","https://openalex.org/W2155160033","https://openalex.org/W2159296364","https://openalex.org/W2162733643","https://openalex.org/W2290431464","https://openalex.org/W2299467264","https://openalex.org/W2319660501","https://openalex.org/W2438471924","https://openalex.org/W2594639291","https://openalex.org/W2734096706","https://openalex.org/W2799272162","https://openalex.org/W2892737606","https://openalex.org/W2900528285","https://openalex.org/W2912031392","https://openalex.org/W2962755172","https://openalex.org/W3021343867","https://openalex.org/W3027404855","https://openalex.org/W3031359560","https://openalex.org/W3038947708","https://openalex.org/W3046450754","https://openalex.org/W3202226255","https://openalex.org/W4290944123","https://openalex.org/W4384525798","https://openalex.org/W6648378535","https://openalex.org/W6659467978","https://openalex.org/W6679901568","https://openalex.org/W6729501165","https://openalex.org/W6750059470","https://openalex.org/W6770641979","https://openalex.org/W6804679018"],"related_works":["https://openalex.org/W2385286892","https://openalex.org/W2350927655","https://openalex.org/W2103761320","https://openalex.org/W2358167375","https://openalex.org/W116564642","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W2078821596","https://openalex.org/W4386563447","https://openalex.org/W1983380679"],"abstract_inverted_index":{"High-quality":[0],"data":[1,37,193],"are":[2,85],"crucial":[3],"for":[4,106,163],"practical":[5],"applications,":[6],"but":[7],"obtaining":[8],"them":[9],"through":[10],"high-precision":[11],"sensors":[12,31,74],"comes":[13],"at":[14,40],"a":[15,95],"high":[16],"cost.":[17,43],"To":[18,89],"guarantee":[19],"the":[20,34,50,64,103,115,121,137,145,149,165,183],"trade-off":[21],"between":[22],"cost":[23],"and":[24,53,79,140,179,185],"precision,":[25],"we":[26,93],"may":[27],"use":[28],"multiple":[29,99],"low-precision":[30,73,191],"to":[32,67,87,119],"obtain":[33],"nearly":[35],"accurate":[36],"fusion":[38,59,104,127,146,154],"results":[39,128],"an":[41],"affordable":[42],"The":[44,109],"commonly":[45],"used":[46],"techniques,":[47],"such":[48,80],"as":[49,102],"Kalman":[51],"filter":[52],"truth":[54],"discovery":[55],"methods,":[56],"typically":[57],"compute":[58],"values":[60],"by":[61,114],"combining":[62,82],"all":[63,83],"observations":[65,84],"according":[66],"predictions":[68],"or":[69],"sensor":[70,100,192],"reliability.":[71],"However,":[72],"can":[75],"often":[76],"cause":[77],"outliers,":[78],"methods":[81,170],"susceptible":[86],"interference.":[88],"handle":[90],"this":[91],"problem,":[92,166],"select":[94],"single":[96],"observation":[97],"from":[98],"readings":[101],"result":[105,147],"each":[107],"timestamp.":[108],"selection":[110],"strategy":[111],"is":[112],"guided":[113],"maximum":[116,150],"likelihood":[117,151],"estimation,":[118],"determine":[120],"most":[122],"probable":[123],"changing":[124],"trends":[125],"of":[126,187],"with":[129,148,171],"adjacent":[130],"timestamps.":[131],"Our":[132],"major":[133],"contributions":[134],"include":[135],"(1)":[136],"problem":[138],"formalization":[139],"NP-hardness":[141],"analysis":[142],"on":[143,160,175],"finding":[144],"w.r.t.":[152],"local":[153],"models,":[155],"(2)":[156],"exact":[157],"algorithms":[158],"based":[159],"dynamic":[161],"programming":[162],"tackling":[164],"(3)":[167],"efficient":[168],"approximation":[169],"performance":[172],"guarantees.":[173],"Experiments":[174],"various":[176],"real":[177],"datasets":[178],"downstream":[180],"applications":[181],"demonstrate":[182],"superiority":[184],"practicality":[186],"our":[188],"work":[189],"in":[190],"fusion.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}