{"id":"https://openalex.org/W2806592424","doi":"https://doi.org/10.1145/3193077.3194509","title":"Automated Mining of Approximate Periodicity on Numeric Data","display_name":"Automated Mining of Approximate Periodicity on Numeric Data","publication_year":2018,"publication_date":"2018-03-23","ids":{"openalex":"https://openalex.org/W2806592424","doi":"https://doi.org/10.1145/3193077.3194509","mag":"2806592424"},"language":"en","primary_location":{"id":"doi:10.1145/3193077.3194509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3193077.3194509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Compute and Data Analysis","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/A5112749024","display_name":"Ran He","orcid":"https://orcid.org/0000-0002-3807-991X"},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ran He","raw_affiliation_strings":["Nokia Bell Labs, NJ"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, NJ","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002854172","display_name":"Sen Yang","orcid":"https://orcid.org/0000-0003-3222-2268"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sen Yang","raw_affiliation_strings":["Rutgers University, NJ"],"affiliations":[{"raw_affiliation_string":"Rutgers University, NJ","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101682022","display_name":"Jingyuan Yang","orcid":"https://orcid.org/0009-0004-4850-6139"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingyuan Yang","raw_affiliation_strings":["Rutgers University, NJ"],"affiliations":[{"raw_affiliation_string":"Rutgers University, NJ","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048855340","display_name":"Jin Cao","orcid":"https://orcid.org/0000-0002-1247-4129"},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin Cao","raw_affiliation_strings":["Nokia Bell Labs, NJ"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, NJ","institution_ids":["https://openalex.org/I72090969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112749024"],"corresponding_institution_ids":["https://openalex.org/I72090969"],"apc_list":null,"apc_paid":null,"fwci":0.1629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57075583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"20","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9771000146865845,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9771000146865845,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9675999879837036,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9624000191688538,"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/granularity","display_name":"Granularity","score":0.8133848905563354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7808583974838257},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.7030123472213745},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6224156022071838},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5974342823028564},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5742136240005493},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5224013328552246},{"id":"https://openalex.org/keywords/carry","display_name":"Carry (investment)","score":0.511874794960022},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.5004227161407471},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43971917033195496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.386766642332077},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34953880310058594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3011370301246643},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1335563063621521}],"concepts":[{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.8133848905563354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7808583974838257},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7030123472213745},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6224156022071838},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5974342823028564},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5742136240005493},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5224013328552246},{"id":"https://openalex.org/C2776299755","wikidata":"https://www.wikidata.org/wiki/Q432449","display_name":"Carry (investment)","level":2,"score":0.511874794960022},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.5004227161407471},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43971917033195496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.386766642332077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34953880310058594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3011370301246643},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1335563063621521},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3193077.3194509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3193077.3194509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Compute and Data Analysis","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":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W27994497","https://openalex.org/W403953909","https://openalex.org/W2006439594","https://openalex.org/W2075186161","https://openalex.org/W2101823270","https://openalex.org/W2115826098","https://openalex.org/W2117149360","https://openalex.org/W2118887058","https://openalex.org/W2126310301","https://openalex.org/W2145672247","https://openalex.org/W2147440465","https://openalex.org/W2148500771","https://openalex.org/W2182361439","https://openalex.org/W2636122354","https://openalex.org/W2770472157","https://openalex.org/W2786029688","https://openalex.org/W4239881334"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W2999756192","https://openalex.org/W101821260","https://openalex.org/W1514923932","https://openalex.org/W2787549830","https://openalex.org/W2041741276","https://openalex.org/W1647555847","https://openalex.org/W2482670354"],"abstract_inverted_index":{"As":[0],"an":[1],"active":[2],"subfield":[3],"of":[4],"Automated":[5],"Machine":[6],"Learning,":[7],"automated":[8,25],"structural":[9,15],"analysis":[10],"focuses":[11],"on":[12,37],"extracting":[13],"the":[14,21,38,103],"information,":[16],"such":[17],"as":[18],"periodicity,":[19],"from":[20,41],"data":[22,26,43,76,89,113],"automatically,":[23],"enabling":[24],"cleaning":[27],"and":[28,47,57,74,94,117],"feature":[29],"extraction.":[30],"Little":[31],"research,":[32],"however,":[33],"has":[34],"been":[35],"done":[36],"periodicity":[39],"mining":[40],"numeric":[42],"that":[44,82,106],"contain":[45],"noises":[46],"missing":[48,118],"points.":[49,120],"In":[50],"this":[51,62],"paper,":[52],"we":[53,68],"present":[54],"a":[55],"practical":[56],"innovative":[58],"framework":[59,84],"to":[60,88,112],"close":[61],"gap.":[63],"To":[64],"validate":[65],"our":[66,83,107],"approach,":[67],"carry":[69],"out":[70],"detailed":[71],"simulation":[72],"studies":[73],"real":[75],"analyses.":[77],"The":[78],"experimental":[79],"results":[80,104],"show":[81],"is":[85,110],"more":[86],"robust":[87],"granularity":[90],"with":[91,99],"better":[92],"accuracy":[93],"computational":[95],"efficiency":[96],"when":[97],"comparing":[98],"baseline":[100],"methods.":[101],"Moreover,":[102],"imply":[105],"proposed":[108],"method":[109],"insensitive":[111],"jitters,":[114],"noise":[115],"points":[116],"signal":[119]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
