{"id":"https://openalex.org/W4315629643","doi":"https://doi.org/10.1109/globecom48099.2022.10000604","title":"On-line Interpolation-based Data Management for Time-Series Data toward Edge Computing","display_name":"On-line Interpolation-based Data Management for Time-Series Data toward Edge Computing","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315629643","doi":"https://doi.org/10.1109/globecom48099.2022.10000604"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10000604","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000604","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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/A5015933802","display_name":"Hiroki Oikawa","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I14396692","display_name":"Tokyo University of Information Sciences","ror":"https://ror.org/044bdx604","country_code":"JP","type":"education","lineage":["https://openalex.org/I14396692"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hiroki Oikawa","raw_affiliation_strings":["The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]},{"raw_affiliation_string":"Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034634831","display_name":"Hangli Ge","orcid":"https://orcid.org/0000-0001-8025-3013"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I14396692","display_name":"Tokyo University of Information Sciences","ror":"https://ror.org/044bdx604","country_code":"JP","type":"education","lineage":["https://openalex.org/I14396692"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hangli Ge","raw_affiliation_strings":["The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]},{"raw_affiliation_string":"Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012068346","display_name":"Noboru Koshizuka","orcid":"https://orcid.org/0000-0002-4728-9425"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I14396692","display_name":"Tokyo University of Information Sciences","ror":"https://ror.org/044bdx604","country_code":"JP","type":"education","lineage":["https://openalex.org/I14396692"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Noboru Koshizuka","raw_affiliation_strings":["The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Interdisciplinary Information Studies,Tokyo,Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]},{"raw_affiliation_string":"Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015933802"],"corresponding_institution_ids":["https://openalex.org/I14396692","https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.1227,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.31911532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"5050","last_page":"5056"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9930999875068665,"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":0.9930999875068665,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9778000116348267,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9704999923706055,"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/computer-science","display_name":"Computer science","score":0.7963941097259521},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6296725273132324},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6020380258560181},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5801569223403931},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.5752068161964417},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5605761408805847},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.546142041683197},{"id":"https://openalex.org/keywords/data-extraction","display_name":"Data extraction","score":0.4886980354785919},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4865376055240631},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46779686212539673},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4611082673072815},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.42940717935562134},{"id":"https://openalex.org/keywords/data-management","display_name":"Data management","score":0.412426620721817},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2627429962158203},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2054998278617859},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13802939653396606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08649078011512756}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7963941097259521},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6296725273132324},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6020380258560181},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5801569223403931},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5752068161964417},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5605761408805847},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.546142041683197},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.4886980354785919},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4865376055240631},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46779686212539673},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4611082673072815},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.42940717935562134},{"id":"https://openalex.org/C1668388","wikidata":"https://www.wikidata.org/wiki/Q1149776","display_name":"Data management","level":2,"score":0.412426620721817},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2627429962158203},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2054998278617859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13802939653396606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08649078011512756},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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},{"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/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10000604","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000604","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2029912643","https://openalex.org/W2130017063","https://openalex.org/W2197305145","https://openalex.org/W2416799949","https://openalex.org/W2787459265","https://openalex.org/W2793389809","https://openalex.org/W2793740190","https://openalex.org/W2914440067","https://openalex.org/W2988431700","https://openalex.org/W3009120490","https://openalex.org/W3170778136","https://openalex.org/W4250955649","https://openalex.org/W6602290650","https://openalex.org/W6737363664"],"related_works":["https://openalex.org/W2092530219","https://openalex.org/W2388464034","https://openalex.org/W2533125852","https://openalex.org/W4214747436","https://openalex.org/W4205595170","https://openalex.org/W2115553997","https://openalex.org/W2119966892","https://openalex.org/W1976221100","https://openalex.org/W298027295","https://openalex.org/W2018533949"],"abstract_inverted_index":{"In":[0,80],"recent":[1],"years,":[2],"a":[3,13,115],"vast":[4],"amount":[5,179],"of":[6,15,19,26,124,139,180,188],"sensor":[7],"information":[8],"has":[9,30,44],"been":[10],"generated":[11],"as":[12],"result":[14],"the":[16,24,36,63,73,77,111,121,125,137,142,151,159,171,178,186,189,199],"widespread":[17],"use":[18],"IoT":[20],"devices.":[21],"However,":[22],"with":[23,114,131,167],"explosion":[25],"data":[27,37,49,75,87,92,100,107,113,133,144,166,181,190],"volume,":[28],"it":[29,43,54,66,130],"become":[31,45],"difficult":[32],"to":[33,47,69,98,101,109,164,176,184,193,198],"process":[34,48],"all":[35,62],"centrally":[38],"on":[39,150],"cloud":[40],"servers.":[41],"Therefore,":[42],"important":[46,74],"at":[50],"edge":[51,58],"devices":[52,59],"where":[53],"is":[55,67,96],"acquired.":[56],"As":[57],"cannot":[60],"store":[61,72],"acquired":[64,78],"data,":[65],"necessary":[68,108],"select":[70],"and":[71,145],"from":[76],"data.":[79,153],"this":[81],"paper,":[82],"we":[83],"propose":[84],"an":[85],"online":[86],"management":[88],"method":[89,127,161],"for":[90],"time-series":[91],"called":[93],"O-IDM,":[94],"which":[95],"designed":[97],"extract":[99,106,165],"preserve":[102],"characteristics.":[103],"O-IDM":[104],"can":[105],"approximate":[110],"original":[112],"small":[116],"computational":[117],"cost.":[118],"We":[119],"evaluated":[120],"extraction":[122,134],"performance":[123],"proposed":[126,160,172],"by":[128,191],"comparing":[129],"other":[132],"methods":[135],"using":[136],"degree":[138],"deviation":[140],"between":[141],"input":[143],"polygonal":[146],"line":[147],"approximation":[148],"base":[149],"extracted":[152],"The":[154],"experimental":[155],"results":[156],"show":[157],"that":[158],"was":[162,174],"able":[163,175],"high":[168],"accuracy.":[169],"Further,":[170],"approach":[173],"reduce":[177],"transmission":[182],"required":[183],"convey":[185],"characteristics":[187],"up":[192],"66.7":[194],"percentage":[195],"point":[196],"compared":[197],"na\u00efve":[200],"time-based":[201],"sampling":[202],"method.":[203]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}