{"id":"https://openalex.org/W14718529","doi":null,"title":"Online periodicity mining","display_name":"Online periodicity mining","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W14718529","mag":"14718529"},"language":"en","primary_location":{"id":"pmh:oai:docs.lib.purdue.edu:dissertations-5709","doi":null,"is_oa":false,"landing_page_url":"https://docs.lib.purdue.edu/dissertations/AAI3185753","pdf_url":null,"source":{"id":"https://openalex.org/S4377196310","display_name":"Purdue e-Pubs (Purdue University System)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801333002","host_organization_name":"Purdue University System","host_organization_lineage":["https://openalex.org/I2801333002"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Theses and Dissertations Available from ProQuest","raw_type":"text"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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/A5111926790","display_name":"Mohamed G. Elfeky","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohamed Elfeky","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089912733","display_name":"Ahmed K. Elmagarmid","orcid":"https://orcid.org/0000-0002-0044-458X","observed_orcids":["https://orcid.org/0000-0002-0044-458X"]},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed K. Elmagarmid","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000123743","display_name":"Walid G. Aref","orcid":"https://orcid.org/0000-0001-8169-7775","observed_orcids":["https://orcid.org/0000-0001-8169-7775"]},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Walid G. Aref","raw_affiliation_strings":["Purdue University"],"raw_orcid":"https://orcid.org/0000-0001-8169-7775","affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9657999873161316,"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":0.9657999873161316,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.03189999982714653,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/periodicity-detection","display_name":"periodicity detection","score":0.8420000076293945},{"id":"https://openalex.org/keywords/time-warping","display_name":"time warping","score":0.8109999895095825},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.746999979019165}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5893741250038147},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5877864360809326},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.5805715322494507},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5767978429794312},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.5736536979675293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5276713967323303},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5132420063018799},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.5071651935577393},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48604780435562134},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46250030398368835},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.44619283080101013},{"id":"https://openalex.org/C120241303","wikidata":"https://www.wikidata.org/wiki/Q3503302","display_name":"Periodic sequence","level":2,"score":0.4282474219799042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2849207818508148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2595999240875244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16442915797233582},{"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},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:docs.lib.purdue.edu:dissertations-5709","doi":null,"is_oa":false,"landing_page_url":"https://docs.lib.purdue.edu/dissertations/AAI3185753","pdf_url":null,"source":{"id":"https://openalex.org/S4377196310","display_name":"Purdue e-Pubs (Purdue University System)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801333002","host_organization_name":"Purdue University System","host_organization_lineage":["https://openalex.org/I2801333002"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Theses and Dissertations Available from ProQuest","raw_type":"text"},{"id":"mag:14718529","doi":null,"is_oa":false,"landing_page_url":"https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=5709&context=dissertations","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6591861836","display_name":"STRUCTURAL GENOMICS OF EUKARYOTIC MODEL ORGANISMS","funder_award_id":"P50GM062413","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2189406657","https://openalex.org/W2314596417","https://openalex.org/W7097919682","https://openalex.org/W2788694945","https://openalex.org/W2387691648","https://openalex.org/W2963021315","https://openalex.org/W2404280840","https://openalex.org/W2148500771","https://openalex.org/W2101823270","https://openalex.org/W2487410748"],"abstract_inverted_index":{"This":[0],"dissertation":[1],"addresses":[2],"the":[3,11,23,45,66,93,95,105,110,116,134,137,144,147,153,187,194,203,216,220,249,256,265,271,286,290,298,301,304,312,319,322],"online":[4,48,83,87,171,196,233],"periodicity":[5,52,60,72,84,117,126,132,135,142,158,165,172,178,234,292,306],"mining":[6,9,53,88,173,179,293],"problem.":[7],"Periodicity":[8],"is":[10],"process":[12],"of":[13,32,70,89,123,136,146,152,190,209,219,222,243,297,303,321],"discovering":[14,58,65],"frequent":[15,67],"periodic":[16,68,90,111,188,309,324],"patterns":[17,69,112,189],"in":[18,26,224,295],"an":[19,100,162],"attempt":[20],"towards":[21],"predicting":[22],"future":[24],"behavior":[25],"time":[27,139,154,204,226,247,257,299],"series":[28,205,227],"data.":[29,212,228,282],"The":[30],"ubiquitousness":[31],"sensor":[33],"devices":[34],"that":[35,175,237,285],"generate":[36],"real-time,":[37],"append-only":[38],"and":[39,64,86,127,181,206,280,308,311],"semi-infinite":[40],"data":[41,102],"streams":[42],"has":[43],"revived":[44],"need":[46],"for":[47,81,269],"processing.":[49],"We":[50,77,229],"define":[51,120],"as":[54],"a":[55,231],"two-step":[56],"process:":[57],"potential":[59],"rates":[61,307],"(Periodicity":[62],"Detection),":[63],"each":[71,157],"rate":[73],"(Mining":[74],"Periodic":[75],"Patterns).":[76],"propose":[78,161,170,230],"new":[79,232],"algorithms":[80,174,197,273,288,294],"both":[82,177,278],"detection":[85,166,235],"patterns.":[91,325],"For":[92,115,156],"latter,":[94],"proposed":[96,195,250,272,287],"algorithm":[97,236,251],"incrementally":[98],"maintains":[99],"efficient":[101,163],"structure,":[103],"namely":[104],"max-subpattern":[106],"tree,":[107],"from":[108],"which":[109],"are":[113,183,274],"discovered.":[114],"detection,":[118],"we":[119,160,169,214],"two":[121],"types":[122,242],"periodicities:":[124],"segment":[125,131],"symbol":[128,141],"periodicity.":[129],"Whereas":[130],"concerns":[133,143],"entire":[138],"series,":[140],"periodicities":[145],"various":[148,260],"symbols":[149],"or":[150,254],"values":[151],"series.":[155],"type,":[159],"convolution-based":[164],"algorithm.":[167],"Furthermore,":[168],"integrate":[176],"steps,":[180],"thus":[182],"able":[184],"to":[185,262,314],"discover":[186],"unknown":[191],"periods.":[192],"All":[193],"require":[198],"only":[199],"one":[200],"pass":[201],"over":[202],"no":[207],"reprocessing":[208],"previously":[210],"seen":[211],"Finally,":[213],"address":[215],"inevitable":[217],"problem":[218],"presence":[221],"noise":[223],"real-world":[225,281],"deals":[238],"efficiently":[239],"with":[240],"all":[241,270],"noise.":[244,266,315],"Based":[245],"on":[246],"warping,":[248],"warps":[252],"(extends":[253],"shrinks)":[255],"axis":[258],"at":[259],"locations":[261],"optimally":[263],"remove":[264],"Experimental":[267],"studies":[268],"carried":[275],"out":[276],"using":[277],"synthetic":[279],"Results":[283],"show":[284],"outperform":[289],"existing":[291],"terms":[296],"performance,":[300],"accuracy":[302],"discovered":[305,323],"patterns,":[310],"resilience":[313],"Real-data":[316],"experiments":[317],"demonstrate":[318],"practicality":[320]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2016-06-24T00:00:00"}
