{"id":"https://openalex.org/W3008071634","doi":"https://doi.org/10.1109/bigdata47090.2019.9006112","title":"Utilizing Multivariate Time Series for Semantic Segmentation","display_name":"Utilizing Multivariate Time Series for Semantic Segmentation","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008071634","doi":"https://doi.org/10.1109/bigdata47090.2019.9006112","mag":"3008071634"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5055184010","display_name":"Frederique van Leeuwen","orcid":"https://orcid.org/0000-0002-0625-9991"},"institutions":[{"id":"https://openalex.org/I193700539","display_name":"Tilburg University","ror":"https://ror.org/04b8v1s79","country_code":"NL","type":"education","lineage":["https://openalex.org/I193700539"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Frederique van Leeuwen","raw_affiliation_strings":["Jheronimus Academy of Data Science, Tilburg University, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Jheronimus Academy of Data Science, Tilburg University, The Netherlands","institution_ids":["https://openalex.org/I193700539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5055184010"],"corresponding_institution_ids":["https://openalex.org/I193700539"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18896526,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6125","last_page":"6127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9968000054359436,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7669352293014526},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7437136769294739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7280464768409729},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6500551700592041},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5486555695533752},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5004880428314209},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4900416135787964},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4827263057231903},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.48062628507614136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47635769844055176},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.44206109642982483},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4407985806465149},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4158622622489929},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4129287004470825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2688145041465759},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1465599536895752}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7669352293014526},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7437136769294739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7280464768409729},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6500551700592041},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5486555695533752},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5004880428314209},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4900416135787964},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4827263057231903},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.48062628507614136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47635769844055176},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44206109642982483},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4407985806465149},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4158622622489929},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4129287004470825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2688145041465759},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1465599536895752},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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":8,"referenced_works":["https://openalex.org/W1991145960","https://openalex.org/W2077760583","https://openalex.org/W2081028405","https://openalex.org/W2105510466","https://openalex.org/W2107633943","https://openalex.org/W2515822248","https://openalex.org/W2583336059","https://openalex.org/W2963460797"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"The":[0,20,36],"rise":[1],"of":[2,15,24,29,38,56,85,96,106],"connected":[3],"devices":[4],"and":[5,22],"new":[6],"communication":[7],"technologies":[8],"has":[9,33,43],"resulted":[10],"in":[11],"an":[12],"enormous":[13],"wealth":[14],"multivariate":[16,39,73],"time":[17,40,58,74,95],"series":[18,41,59,75],"data.":[19],"identification":[21],"extraction":[23],"meaningful":[25],"segments":[26],"by":[27],"means":[28],"data":[30],"mining":[31],"algorithms":[32,99],"many":[34],"applications.":[35],"problem":[37],"segmentation":[42],"been":[44],"studied":[45],"extensively":[46],"with":[47],"statistical":[48,54],"methods":[49],"that":[50,90],"rely":[51],"on":[52],"the":[53,57,72,83,93,104],"properties":[55],"for":[60,76],"segmentation.":[61,77],"We":[62],"introduce":[63],"a":[64,79],"novel":[65],"method,":[66],"which":[67],"exploits":[68],"domain-specific":[69],"information":[70],"from":[71],"As":[78],"proof-of-principle,":[80],"we":[81],"demonstrate":[82],"feasibility":[84],"our":[86],"method.":[87],"Results":[88],"show":[89],"after":[91],"segmentation,":[92],"running":[94],"anomaly":[97,107],"detection":[98],"reduces":[100],"significantly,":[101],"while":[102],"preserving":[103],"effectiveness":[105],"detection.":[108]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}