{"id":"https://openalex.org/W4406461231","doi":"https://doi.org/10.1109/bigdata62323.2024.10825192","title":"Enhancing Big Data Analysis: A Recursive Window Segmentation Strategy for Multivariate Longitudinal Data","display_name":"Enhancing Big Data Analysis: A Recursive Window Segmentation Strategy for Multivariate Longitudinal Data","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461231","doi":"https://doi.org/10.1109/bigdata62323.2024.10825192"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5106011084","display_name":"Desmond Fomo","orcid":null},"institutions":[{"id":"https://openalex.org/I89630735","display_name":"Yokohama City University","ror":"https://ror.org/0135d1r83","country_code":"JP","type":"education","lineage":["https://openalex.org/I89630735"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Desmond Fomo","raw_affiliation_strings":["Yokohama City University,Graduate School of Data Science,Department of Data Science,Yokohama,Japan"],"affiliations":[{"raw_affiliation_string":"Yokohama City University,Graduate School of Data Science,Department of Data Science,Yokohama,Japan","institution_ids":["https://openalex.org/I89630735"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101531043","display_name":"Akihiro Sato","orcid":"https://orcid.org/0000-0001-7410-6324"},"institutions":[{"id":"https://openalex.org/I89630735","display_name":"Yokohama City University","ror":"https://ror.org/0135d1r83","country_code":"JP","type":"education","lineage":["https://openalex.org/I89630735"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Aki-Hiro Sato","raw_affiliation_strings":["Yokohama City University,Graduate School of Data Science,Department of Data Science,Yokohama,Japan"],"affiliations":[{"raw_affiliation_string":"Yokohama City University,Graduate School of Data Science,Department of Data Science,Yokohama,Japan","institution_ids":["https://openalex.org/I89630735"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5106011084"],"corresponding_institution_ids":["https://openalex.org/I89630735"],"apc_list":null,"apc_paid":null,"fwci":0.375,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62271121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"870","last_page":"879"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9941999912261963,"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":0.9941999912261963,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9861000180244446,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9851999878883362,"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/multivariate-statistics","display_name":"Multivariate statistics","score":0.7499215602874756},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.718989372253418},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.5702976584434509},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.525798499584198},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5215463638305664},{"id":"https://openalex.org/keywords/longitudinal-data","display_name":"Longitudinal data","score":0.5213590860366821},{"id":"https://openalex.org/keywords/multivariate-analysis","display_name":"Multivariate analysis","score":0.5069015622138977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4944305419921875},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45721766352653503},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3494930863380432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27520447969436646}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7499215602874756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.718989372253418},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.5702976584434509},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.525798499584198},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5215463638305664},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.5213590860366821},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.5069015622138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4944305419921875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45721766352653503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3494930863380432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27520447969436646},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1536447791","https://openalex.org/W1964522863","https://openalex.org/W1999996900","https://openalex.org/W2000031724","https://openalex.org/W2024753568","https://openalex.org/W2034139177","https://openalex.org/W2057622893","https://openalex.org/W2059891554","https://openalex.org/W2143991132","https://openalex.org/W2149140091","https://openalex.org/W2262233437","https://openalex.org/W2303147257","https://openalex.org/W2518582440","https://openalex.org/W2591382767","https://openalex.org/W2604829132","https://openalex.org/W2606543275","https://openalex.org/W2784499877","https://openalex.org/W2802925146","https://openalex.org/W2944477354","https://openalex.org/W2949351602","https://openalex.org/W3029164446","https://openalex.org/W3044719873","https://openalex.org/W3098949126","https://openalex.org/W3121461758","https://openalex.org/W4200271341","https://openalex.org/W4200390735","https://openalex.org/W4213099427","https://openalex.org/W4213251304","https://openalex.org/W4250857377","https://openalex.org/W4328022249","https://openalex.org/W4386148303","https://openalex.org/W4387166149","https://openalex.org/W4401110314","https://openalex.org/W6632102171","https://openalex.org/W6785705305"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W40745829","https://openalex.org/W4318262572","https://openalex.org/W1978357124","https://openalex.org/W1578824628","https://openalex.org/W2032728545","https://openalex.org/W1570805059","https://openalex.org/W4250754046","https://openalex.org/W4243682621","https://openalex.org/W2036849593"],"abstract_inverted_index":{"The":[0],"rapid":[1],"growth":[2],"of":[3,47,59,99],"multivariate":[4,67],"longitudinal":[5],"data":[6,93,100],"across":[7,71],"diverse":[8],"industries":[9],"necessitates":[10],"advanced":[11],"analytical":[12],"strategies":[13],"for":[14,66,91],"uncovering":[15],"complex":[16],"patterns":[17],"and":[18,40,75,102],"enhancing":[19],"predictive":[20],"accuracy.":[21],"This":[22,84],"paper":[23],"introduces":[24],"an":[25],"adaptive":[26],"window":[27],"segmentation":[28,64],"strategy":[29],"that":[30],"dynamically":[31],"adjusts":[32],"based":[33],"on":[34],"statistical":[35],"variability,":[36],"including":[37],"covariance,":[38],"skewness,":[39],"kurtosis,":[41],"tailored":[42],"to":[43],"the":[44,53,57,72,96],"unique":[45],"characteristics":[46],"industry-specific":[48],"datasets.":[49],"Extending":[50],"prior":[51],"work,":[52],"enhanced":[54],"methodology":[55],"overcomes":[56],"limitations":[58],"traditional":[60],"approaches":[61],"by":[62],"optimizing":[63],"parameters":[65],"contexts.":[68],"Empirical":[69],"evaluations":[70],"finance,":[73],"retail,":[74],"healthcare":[76],"sectors":[77],"demonstrate":[78],"significant":[79],"improvements":[80],"in":[81],"forecasting":[82],"precision.":[83],"work":[85],"provides":[86],"a":[87],"scalable,":[88],"context-aware":[89],"solution":[90],"big":[92],"analytics,":[94],"refining":[95],"quantitative":[97],"boundaries":[98],"bigness":[101],"enabling":[103],"more":[104],"effective,":[105],"data-driven":[106],"decision-making.":[107]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
