{"id":"https://openalex.org/W2764033112","doi":"https://doi.org/10.1109/nvmsa.2017.8064474","title":"Downsampling of time-series data for approximated dynamic time warping on nonvolatile memories","display_name":"Downsampling of time-series data for approximated dynamic time warping on nonvolatile memories","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2764033112","doi":"https://doi.org/10.1109/nvmsa.2017.8064474","mag":"2764033112"},"language":"en","primary_location":{"id":"doi:10.1109/nvmsa.2017.8064474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nvmsa.2017.8064474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 6th Non-Volatile Memory Systems and Applications Symposium (NVMSA)","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/A5009073299","display_name":"Xingni Li","orcid":"https://orcid.org/0000-0002-6395-1706"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingni Li","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109182727","display_name":"Yi Gu","orcid":"https://orcid.org/0000-0001-5389-3998"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Gu","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015927991","display_name":"Po\u2010Chun Huang","orcid":"https://orcid.org/0000-0003-1076-2271"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Po-Chun Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675843","display_name":"Duo Liu","orcid":"https://orcid.org/0000-0002-3040-2065"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duo Liu","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100426918","display_name":"Liang Liang","orcid":"https://orcid.org/0000-0002-2778-455X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Liang","raw_affiliation_strings":["College of Communication Engineering, Chongqing University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3735,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58946363,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11309","display_name":"Music and Audio Processing","score":0.9965999722480774,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9689000248908997,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.9671807885169983},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7790860533714294},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7007652521133423},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6418683528900146},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.5787776708602905},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5227951407432556},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4341740608215332},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4323367476463318},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41553518176078796},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36271852254867554},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34171462059020996},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2383449673652649}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9671807885169983},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7790860533714294},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7007652521133423},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6418683528900146},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5787776708602905},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5227951407432556},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4341740608215332},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4323367476463318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41553518176078796},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36271852254867554},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34171462059020996},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2383449673652649},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nvmsa.2017.8064474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nvmsa.2017.8064474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 6th Non-Volatile Memory Systems and Applications Symposium (NVMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W58346954","https://openalex.org/W89823361","https://openalex.org/W116902681","https://openalex.org/W131856359","https://openalex.org/W1534304300","https://openalex.org/W1966554111","https://openalex.org/W1972677429","https://openalex.org/W1973965874","https://openalex.org/W1976635144","https://openalex.org/W2078059979","https://openalex.org/W2088136385","https://openalex.org/W2089988906","https://openalex.org/W2106595237","https://openalex.org/W2108752797","https://openalex.org/W2109453625","https://openalex.org/W2119014534","https://openalex.org/W2120858595","https://openalex.org/W2133184712","https://openalex.org/W2134560790","https://openalex.org/W2137847645","https://openalex.org/W2144994235","https://openalex.org/W2156404139","https://openalex.org/W2164463707","https://openalex.org/W2191921326","https://openalex.org/W2502493626","https://openalex.org/W2593841084","https://openalex.org/W2599162857","https://openalex.org/W2963393865","https://openalex.org/W4206005576","https://openalex.org/W4245403080","https://openalex.org/W4285719527","https://openalex.org/W6603735688","https://openalex.org/W6604828220"],"related_works":["https://openalex.org/W3111157199","https://openalex.org/W3118503757","https://openalex.org/W2760717005","https://openalex.org/W2014214435","https://openalex.org/W2163118894","https://openalex.org/W58346954","https://openalex.org/W2359638073","https://openalex.org/W3009759344","https://openalex.org/W1545076181","https://openalex.org/W2086226917"],"abstract_inverted_index":{"In":[0,141,169],"recent":[1],"years,":[2],"time-series":[3,26,42,95,98,150],"data":[4,99,134,151],"have":[5,73],"emerged":[6],"in":[7,78],"a":[8,106,146,175,198],"variety":[9],"of":[10,64,93,100,109,126,162,191,200],"application":[11],"domains,":[12],"such":[13],"as":[14],"wireless":[15],"sensor":[16],"networks":[17],"and":[18,31,112,115],"surveillance":[19],"systems.":[20],"To":[21],"identify":[22],"the":[23,28,39,45,57,62,91,94,118,124,160,163,179,192,204],"similarity":[24],"between":[25,41],"data,":[27],"Euclidean":[29,46],"distance":[30],"its":[32,51],"variations":[33],"are":[34,48,183,206],"common":[35],"metrics":[36],"that":[37,182],"quantify":[38],"differences":[40],"data.":[43,96],"However,":[44,97],"distances":[47],"limited":[49,138],"by":[50,90,166,186,197],"inability":[52],"to":[53,148,152,177],"elastically":[54],"shift":[55],"with":[56,137],"time":[58,66],"axis,":[59],"which":[60],"motivates":[61],"development":[63],"dynamic":[65],"warping":[67],"(DTW)":[68],"algorithms.":[69,168,188],"While":[70],"DTW":[71,119,127,167,187],"algorithms":[72],"been":[74],"proven":[75],"very":[76],"useful":[77],"diversified":[79],"applications":[80],"like":[81],"speech":[82],"recognition,":[83],"their":[84,155],"efficacy":[85,190],"might":[86,103],"be":[87],"seriously":[88,158],"affected":[89],"resolution":[92,102],"high":[101],"take":[104],"up":[105],"gigantic":[107],"amount":[108],"main":[110],"memory":[111],"storage":[113],"space,":[114],"slow":[116],"down":[117],"analysis":[120,128],"procedure.":[121],"This":[122],"makes":[123],"upscaling":[125],"more":[129],"challenging,":[130],"especially":[131],"for":[132],"in-memory":[133],"analytics":[135],"platforms":[136],"NVM":[139],"space.":[140],"this":[142,172],"work,":[143],"we":[144],"propose":[145],"strategy":[147],"downsample":[149],"significantly":[153],"reduce":[154],"size":[156],"without":[157],"affecting":[159],"precision":[161],"results":[164,205],"obtained":[165],"other":[170],"words,":[171],"work":[173],"proposes":[174],"technique":[176,194],"remove":[178],"unimportant":[180],"details":[181],"largely":[184],"ignored":[185],"The":[189],"proposed":[193],"is":[195],"verified":[196],"series":[199],"experimental":[201],"studies,":[202],"where":[203],"quite":[207],"encouraging.":[208]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
