{"id":"https://openalex.org/W3008946857","doi":"https://doi.org/10.1109/bigdata47090.2019.9006483","title":"Novel Online Algorithms for Nonparametric Correlations with Application to Analyze Sensor Data","display_name":"Novel Online Algorithms for Nonparametric Correlations with Application to Analyze Sensor Data","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008946857","doi":"https://doi.org/10.1109/bigdata47090.2019.9006483","mag":"3008946857"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006483","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006483","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5101703681","display_name":"Xiao Wei","orcid":"https://orcid.org/0000-0002-6258-6129"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Xiao","raw_affiliation_strings":["Customer Service Application Amazon, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Customer Service Application Amazon, Seattle, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101703681"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":1.6388,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86357467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"404","last_page":"412"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9824000000953674,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9596999883651733,"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/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.8022788763046265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6602147221565247},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6406586766242981},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4295586049556732},{"id":"https://openalex.org/keywords/online-algorithm","display_name":"Online algorithm","score":0.4144136905670166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21672189235687256},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13070285320281982}],"concepts":[{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.8022788763046265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6602147221565247},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6406586766242981},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4295586049556732},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.4144136905670166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21672189235687256},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13070285320281982},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006483","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006483","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1529840045","https://openalex.org/W1537430301","https://openalex.org/W1985514943","https://openalex.org/W2002070806","https://openalex.org/W2025440555","https://openalex.org/W2030326671","https://openalex.org/W2048957655","https://openalex.org/W2068844322","https://openalex.org/W2103609600","https://openalex.org/W2129249398","https://openalex.org/W2169423212","https://openalex.org/W2293050121","https://openalex.org/W2319525095","https://openalex.org/W2498631646","https://openalex.org/W2591340095","https://openalex.org/W2962784560","https://openalex.org/W3020866151","https://openalex.org/W3103493134","https://openalex.org/W4239181501","https://openalex.org/W4285719527","https://openalex.org/W6684549341","https://openalex.org/W6734055212"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4243114048","https://openalex.org/W2529605301","https://openalex.org/W4237896776","https://openalex.org/W4231665652","https://openalex.org/W1837630526","https://openalex.org/W2000242494","https://openalex.org/W2335589441","https://openalex.org/W4296826658","https://openalex.org/W1979697693"],"abstract_inverted_index":{"Nonparametric":[0],"correlations":[1,27,131],"such":[2],"as":[3],"Spearman's":[4],"rank":[5],"correlation":[6,10],"and":[7,16,84,89,100,112,142],"Kendall's":[8],"tau":[9],"are":[11,37,103],"widely":[12],"applied":[13,161],"in":[14,61,121],"scientific":[15],"engineering":[17],"fields.":[18],"This":[19,66],"paper":[20,67],"investigates":[21],"the":[22,29,55,62,116,122,129,138],"problem":[23],"of":[24,118],"computing":[25,74],"nonparametric":[26,75,130],"on":[28,149,154],"fly":[30],"for":[31,73,93],"streaming":[32],"data.":[33],"Standard":[34],"batch":[35,140],"algorithms":[36],"generally":[38],"too":[39,50],"slow":[40],"to":[41,58,133,162],"handle":[42],"real-world":[43,164],"big":[44],"data":[45,56,166],"applications.":[46],"They":[47],"also":[48],"require":[49],"much":[51],"memory":[52,63,87,99],"because":[53],"all":[54,150],"need":[57],"be":[59],"stored":[60],"before":[64],"processing.":[65],"proposes":[68],"a":[69,108],"novel":[70],"online":[71,125],"algorithm":[72,78,126,159],"correlations.":[76],"The":[77,124,158],"has":[79],"O":[80,85],"(1)":[81,86],"time":[82],"complexity":[83],"cost":[88],"is":[90,160],"quite":[91],"suitable":[92],"edge":[94],"devices,":[95],"where":[96],"only":[97],"limited":[98],"processing":[101],"power":[102],"available.":[104],"You":[105],"can":[106,127,144],"seek":[107],"balance":[109],"between":[110],"speed":[111],"accuracy":[113],"by":[114],"changing":[115],"number":[117],"cut-points":[119],"specified":[120],"algorithm.":[123],"compute":[128,145],"10":[132],"1,000":[134],"times":[135],"faster":[136],"than":[137],"corresponding":[139],"algorithm,":[141],"it":[143],"them":[146],"based":[147],"either":[148],"past":[151],"observations":[152],"or":[153],"fixed-size":[155],"sliding":[156],"windows.":[157],"analyze":[163],"sensor":[165],"from":[167],"an":[168],"industrial":[169],"plant":[170],"<sup":[171],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[172],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[173],".":[174]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
