{"id":"https://openalex.org/W4234102748","doi":"https://doi.org/10.1109/icpr.2004.1334609","title":"An advanced segmental semi-Markov model based online series pattern detection","display_name":"An advanced segmental semi-Markov model based online series pattern detection","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W4234102748","doi":"https://doi.org/10.1109/icpr.2004.1334609"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2004.1334609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2004.1334609","pdf_url":null,"source":{"id":"https://openalex.org/S4363608750","display_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101025164","display_name":"Sen Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Jia","raw_affiliation_strings":["Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","College of Computer Science, University of Zhejiang, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"College of Computer Science, University of Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059857918","display_name":"Yuntao Qian","orcid":"https://orcid.org/0000-0002-7418-5891"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuntao Qian","raw_affiliation_strings":["Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","College of Computer Science, University of Zhejiang, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"College of Computer Science, University of Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102152323","display_name":"Guang Dai","orcid":"https://orcid.org/0000-0002-3529-9087"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Dai","raw_affiliation_strings":["Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","College of Computer Science, University of Zhejiang, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"College of Computer Science, University of Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.2594,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51324965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"uci ics 1 54","issue":null,"first_page":"634","last_page":"637 Vol.3"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9728999733924866,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9704999923706055,"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/sliding-window-protocol","display_name":"Sliding window protocol","score":0.8168936967849731},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.725638747215271},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.7219305038452148},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6995242834091187},{"id":"https://openalex.org/keywords/subsequence","display_name":"Subsequence","score":0.6610455513000488},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.580730140209198},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.5269516110420227},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5239636898040771},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5194715857505798},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4943521022796631},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.49141061305999756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48158740997314453},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4688350558280945},{"id":"https://openalex.org/keywords/longest-common-subsequence-problem","display_name":"Longest common subsequence problem","score":0.45340695977211},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45280033349990845},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.44212692975997925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3511884808540344},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3393802046775818},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.2974693477153778},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24528905749320984},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16718783974647522},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.12088984251022339},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11415499448776245},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09462743997573853}],"concepts":[{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.8168936967849731},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.725638747215271},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.7219305038452148},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6995242834091187},{"id":"https://openalex.org/C137877099","wikidata":"https://www.wikidata.org/wiki/Q1332977","display_name":"Subsequence","level":3,"score":0.6610455513000488},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.580730140209198},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5269516110420227},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5239636898040771},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5194715857505798},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4943521022796631},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.49141061305999756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48158740997314453},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4688350558280945},{"id":"https://openalex.org/C120098539","wikidata":"https://www.wikidata.org/wiki/Q141001","display_name":"Longest common subsequence problem","level":2,"score":0.45340695977211},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45280033349990845},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.44212692975997925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3511884808540344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3393802046775818},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.2974693477153778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24528905749320984},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16718783974647522},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.12088984251022339},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11415499448776245},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09462743997573853},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2004.1334609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2004.1334609","pdf_url":null,"source":{"id":"https://openalex.org/S4363608750","display_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1970530643","https://openalex.org/W2007321142","https://openalex.org/W2072054026","https://openalex.org/W2076249942","https://openalex.org/W2083393647","https://openalex.org/W2107633943","https://openalex.org/W2125838338"],"related_works":["https://openalex.org/W3027372070","https://openalex.org/W25732909","https://openalex.org/W2554075302","https://openalex.org/W4323338832","https://openalex.org/W4289596129","https://openalex.org/W3145461922","https://openalex.org/W170643605","https://openalex.org/W1999879627","https://openalex.org/W1964543066","https://openalex.org/W2077484872"],"abstract_inverted_index":{"The":[0],"online":[1,67],"pattern":[2,68],"detection":[3],"technology":[4],"is":[5,26,61,91,115,126],"an":[6],"important":[7],"part":[8],"of":[9,49,66,97],"the":[10,47,57,64,71,95,98,110,119],"time":[11,35],"series":[12],"analysis,":[13],"and":[14,33,51,100,124,135],"some":[15,87],"methods":[16,80,102],"have":[17,46],"been":[18],"proposed,":[19],"in":[20,81],"which":[21],"subsequence":[22,42],"matching":[23],"based":[24,59],"window-sliding":[25],"popularly":[27],"applied.":[28],"For":[29],"window-sliding,":[30],"Euclidean":[31],"distance":[32],"dynamic":[34],"warping":[36],"(DTW)":[37],"are":[38,103],"always":[39],"used":[40],"as":[41],"matching,":[43],"but":[44],"they":[45],"drawbacks":[48],"sensitivity":[50],"expensive":[52],"computational":[53],"load":[54],"respectively.":[55],"Recently,":[56],"model":[58,74,114,123],"method":[60],"introduced":[62],"into":[63],"field":[65],"detection,":[69],"especially,":[70],"segmental":[72,112,121],"semi-Markov":[73,113,122],"shows":[75],"better":[76],"performance":[77],"than":[78],"sliding":[79],"many":[82],"aspects.":[83],"However,":[84],"it":[85,90,125],"has":[86],"limitations,":[88],"e.g.,":[89],"difficult":[92],"to":[93,117],"estimate":[94],"parameters":[96],"model,":[99],"nowaday":[101],"too":[104],"rough,":[105],"etc.":[106],"In":[107],"this":[108],"paper,":[109],"advanced":[111],"proposed":[116],"improve":[118],"existed":[120],"successfully":[127],"demonstrated":[128],"on":[129],"real":[130],"data":[131],"sets,":[132],"including":[133],"financial":[134],"medical":[136],"data.":[137]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
