{"id":"https://openalex.org/W1989477967","doi":"https://doi.org/10.1007/s10489-014-0553-x","title":"Analyzing very large time series using suffix arrays","display_name":"Analyzing very large time series using suffix arrays","publication_year":2014,"publication_date":"2014-08-22","ids":{"openalex":"https://openalex.org/W1989477967","doi":"https://doi.org/10.1007/s10489-014-0553-x","mag":"1989477967"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-014-0553-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-014-0553-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-014-0553-x.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-014-0553-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083041537","display_name":"Konstantinos F. Xylogiannopoulos","orcid":"https://orcid.org/0000-0003-2376-898X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Konstantinos F. Xylogiannopoulos","raw_affiliation_strings":["Department of Computer Science, University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034281379","display_name":"Panagiotis Karampelas","orcid":"https://orcid.org/0000-0003-1684-7612"},"institutions":[{"id":"https://openalex.org/I25055547","display_name":"Hellenic American University","ror":"https://ror.org/0423r3m86","country_code":"US","type":"education","lineage":["https://openalex.org/I25055547"]},{"id":"https://openalex.org/I2802113776","display_name":"Hellenic Air Force","ror":"https://ror.org/044xk2674","country_code":"GR","type":"government","lineage":["https://openalex.org/I2802113776"]}],"countries":["GR","US"],"is_corresponding":false,"raw_author_name":"Panagiotis Karampelas","raw_affiliation_strings":["Department of Informatics & Computers, Hellenic Air Force Academy, Attica, Greece","Department of Information Technology, Hellenic American University, Manchester, NH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics & Computers, Hellenic Air Force Academy, Attica, Greece","institution_ids":["https://openalex.org/I2802113776"]},{"raw_affiliation_string":"Department of Information Technology, Hellenic American University, Manchester, NH, USA","institution_ids":["https://openalex.org/I25055547"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066265052","display_name":"Reda Alhajj","orcid":"https://orcid.org/0000-0001-6657-9738"},"institutions":[{"id":"https://openalex.org/I11923345","display_name":"Wroc\u0142aw University of Science and Technology","ror":"https://ror.org/008fyn775","country_code":"PL","type":"education","lineage":["https://openalex.org/I11923345"]},{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]},{"id":"https://openalex.org/I904090387","display_name":"Global University","ror":"https://ror.org/04frhb927","country_code":"LB","type":"education","lineage":["https://openalex.org/I904090387"]}],"countries":["CA","LB","PL"],"is_corresponding":true,"raw_author_name":"Reda Alhajj","raw_affiliation_strings":["Department of Computer Science, Global University, Beirut, Lebanon","Department of Computer Science, University of Calgary, Calgary, Alberta, Canada","Wroclaw University of Technology, Institute of Informatics, Wroclaw, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Global University, Beirut, Lebanon","institution_ids":["https://openalex.org/I904090387"]},{"raw_affiliation_string":"Department of Computer Science, University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"Wroclaw University of Technology, Institute of Informatics, Wroclaw, Poland","institution_ids":["https://openalex.org/I11923345"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5066265052"],"corresponding_institution_ids":["https://openalex.org/I11923345","https://openalex.org/I168635309","https://openalex.org/I904090387"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":5.8131,"has_fulltext":true,"cited_by_count":36,"citation_normalized_percentile":{"value":0.95715146,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"41","issue":"3","first_page":"941","last_page":"955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":1.0,"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/T11269","display_name":"Algorithms and Data Compression","score":1.0,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.9858999848365784,"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/computer-science","display_name":"Computer science","score":0.8062632083892822},{"id":"https://openalex.org/keywords/suffix","display_name":"Suffix","score":0.7697280645370483},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6932054758071899},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6148082613945007},{"id":"https://openalex.org/keywords/pattern-matching","display_name":"Pattern matching","score":0.5746684670448303},{"id":"https://openalex.org/keywords/string-searching-algorithm","display_name":"String searching algorithm","score":0.5584760904312134},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.554172158241272},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.5300255417823792},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.4873264729976654},{"id":"https://openalex.org/keywords/suffix-tree","display_name":"Suffix tree","score":0.4522550106048584},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44298967719078064},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3919878900051117},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32798606157302856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3160989284515381},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.245401531457901},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1576959192752838},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13274309039115906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8062632083892822},{"id":"https://openalex.org/C2779804580","wikidata":"https://www.wikidata.org/wiki/Q102047","display_name":"Suffix","level":2,"score":0.7697280645370483},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6932054758071899},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6148082613945007},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.5746684670448303},{"id":"https://openalex.org/C7757238","wikidata":"https://www.wikidata.org/wiki/Q374040","display_name":"String searching algorithm","level":3,"score":0.5584760904312134},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.554172158241272},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.5300255417823792},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.4873264729976654},{"id":"https://openalex.org/C2781166958","wikidata":"https://www.wikidata.org/wiki/Q1426863","display_name":"Suffix tree","level":3,"score":0.4522550106048584},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44298967719078064},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3919878900051117},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32798606157302856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3160989284515381},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.245401531457901},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1576959192752838},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13274309039115906},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-014-0553-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-014-0553-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-014-0553-x.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-014-0553-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-014-0553-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-014-0553-x.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1989477967.pdf","grobid_xml":"https://content.openalex.org/works/W1989477967.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W324416766","https://openalex.org/W938539187","https://openalex.org/W1485847476","https://openalex.org/W1495956867","https://openalex.org/W1726445723","https://openalex.org/W1969173824","https://openalex.org/W1974956214","https://openalex.org/W1981718788","https://openalex.org/W1988109548","https://openalex.org/W1990061958","https://openalex.org/W2005833134","https://openalex.org/W2006439594","https://openalex.org/W2013722099","https://openalex.org/W2029955663","https://openalex.org/W2032552117","https://openalex.org/W2046798798","https://openalex.org/W2059513841","https://openalex.org/W2073984053","https://openalex.org/W2077333702","https://openalex.org/W2082932857","https://openalex.org/W2094699183","https://openalex.org/W2105709506","https://openalex.org/W2118703123","https://openalex.org/W2121252285","https://openalex.org/W2124348703","https://openalex.org/W2126310301","https://openalex.org/W2133432734","https://openalex.org/W2148500771","https://openalex.org/W2167294216","https://openalex.org/W2171446221","https://openalex.org/W2504545875","https://openalex.org/W2533248932","https://openalex.org/W2610179052"],"related_works":["https://openalex.org/W2351290558","https://openalex.org/W2355168454","https://openalex.org/W1993696469","https://openalex.org/W117230746","https://openalex.org/W1579398817","https://openalex.org/W2374526264","https://openalex.org/W1543802159","https://openalex.org/W1811426704","https://openalex.org/W1578828659","https://openalex.org/W1981718788"],"abstract_inverted_index":{"Suffix":[0],"arrays":[1,63],"form":[2],"a":[3,13,18,36,260,270],"powerful":[4],"data":[5],"structure":[6],"for":[7,48,65,145,264],"pattern":[8,103,237],"detection":[9,30,104],"and":[10,111,121,131,229,240,277],"matching.":[11],"In":[12],"previous":[14],"work,":[15],"we":[16,254],"presented":[17],"novel":[19],"algorithm":[20,26,239],"(COV)":[21],"which":[22,245],"is":[23,171,212,259],"the":[24,29,41,46,50,59,76,85,114,125,128,146,161,189,197,206,214,225],"only":[25,123],"that":[27,74,257],"allows":[28,84],"of":[31,61,78,116,127,148,153,196,208,274],"all":[32,266],"repeated":[33,102,209,267],"patterns":[34,268],"in":[35,91,167,248,269],"time":[37,68,109,118,151,177,221,251,271],"series":[38,119,152,178,272],"by":[39,193],"using":[40,75,105,234],"actual":[42,51,86],"suffix":[43,52,62],"array.":[44],"However,":[45],"requirements":[47],"storing":[49],"strings":[53],"even":[54],"on":[55,98],"external":[56,99,129],"media":[57,130],"makes":[58,113],"use":[60],"impossible":[64],"very":[66,149,175,261],"large":[67,117,176],"series.":[69],"We":[70],"have":[71],"already":[72],"proved":[73],"concept":[77],"Longest":[79],"Expected":[80],"Repeated":[81],"Pattern":[82],"(LERP)":[83],"suffices":[87],"to":[88,124,156,173,205,224],"be":[89,142],"stored":[90],"linear":[92],"capacity":[93],"O":[94],"(":[95],"n":[96],")":[97],"media.":[100],"The":[101],"LERP":[106,163,190,227],"has":[107],"analogous":[108],"complexity,":[110],"thus":[112],"analysis":[115,147],"feasible":[120],"limited":[122],"size":[126,154,180,276],"not":[132],"memory.":[133],"Yet,":[134],"there":[135],"are":[136,246],"cases":[137],"when":[138,201],"hardware":[139,278],"limitations":[140],"might":[141],"an":[143],"obstacle":[144],"larger":[150,186],"comparable":[155],"hard":[157],"disk":[158],"capacity.":[159],"With":[160],"Moving":[162],"(MLERP)":[164,217],"method":[165,216,228],"introduced":[166],"this":[168],"paper,":[169],"it":[170],"possible":[172],"analyze":[174],"(of":[179],"tens":[181],"or":[182],"hundreds":[183],"thousands":[184],"times":[185],"than":[187,233],"what":[188],"can":[191,218],"analyze)":[192],"maximal":[194],"utilization":[195],"available":[198],"hardware.":[199],"Further,":[200],"empirical":[202],"knowledge":[203],"related":[204],"distribution":[207],"pattern\u2019s":[210],"length":[211],"available,":[213],"proposed":[215],"achieve":[219],"better":[220,232],"performance":[222],"compared":[223],"standard":[226],"definitely":[230],"much":[231],"any":[235],"other":[236],"matching":[238],"applying":[241],"brute":[242],"force":[243],"techniques":[244],"unfeasible":[247],"logical":[249],"(human)":[250],"frame.":[252],"Thus,":[253],"may":[255],"argue":[256],"MLERP":[258],"useful":[262],"tool":[263],"detecting":[265],"regardless":[273],"its":[275],"limitations.":[279]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}