{"id":"https://openalex.org/W4319782125","doi":"https://doi.org/10.1109/dsaa54385.2022.10032331","title":"COSTI: a New Classifier for Sequences of Temporal Intervals","display_name":"COSTI: a New Classifier for Sequences of Temporal Intervals","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319782125","doi":"https://doi.org/10.1109/dsaa54385.2022.10032331"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032331","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032331","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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/A5028988344","display_name":"Jakub Michal Bilski","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Jakub Michal Bilski","raw_affiliation_strings":["Warsaw University of Technology,Faculty of Mathematics and Information Science,Warsaw,Poland","Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Warsaw University of Technology,Faculty of Mathematics and Information Science,Warsaw,Poland","institution_ids":["https://openalex.org/I108403487"]},{"raw_affiliation_string":"Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033442768","display_name":"Agnieszka Jastrz\u0119bska","orcid":"https://orcid.org/0000-0001-5361-5787"},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Agnieszka Jastrzebska","raw_affiliation_strings":["Warsaw University of Technology,Faculty of Mathematics and Information Science,Warsaw,Poland","Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Warsaw University of Technology,Faculty of Mathematics and Information Science,Warsaw,Poland","institution_ids":["https://openalex.org/I108403487"]},{"raw_affiliation_string":"Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028988344"],"corresponding_institution_ids":["https://openalex.org/I108403487"],"apc_list":null,"apc_paid":null,"fwci":0.2455,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.45624013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"42","issue":null,"first_page":"1","last_page":"10"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9611999988555908,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9606999754905701,"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/computer-science","display_name":"Computer science","score":0.7243791222572327},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.64217609167099},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.526945948600769},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.496725857257843},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.4816807210445404},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47360801696777344},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4512821137905121},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4333987236022949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4186147451400757},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.41350215673446655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35404759645462036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243791222572327},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.64217609167099},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.526945948600769},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.496725857257843},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.4816807210445404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47360801696777344},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4512821137905121},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4333987236022949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4186147451400757},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.41350215673446655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35404759645462036},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/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/dsaa54385.2022.10032331","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032331","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":41,"referenced_works":["https://openalex.org/W75479076","https://openalex.org/W148444222","https://openalex.org/W1929083272","https://openalex.org/W1959085111","https://openalex.org/W2022018348","https://openalex.org/W2033268097","https://openalex.org/W2039105019","https://openalex.org/W2058151187","https://openalex.org/W2069720347","https://openalex.org/W2074420638","https://openalex.org/W2082285428","https://openalex.org/W2086019232","https://openalex.org/W2101234009","https://openalex.org/W2161484642","https://openalex.org/W2240212332","https://openalex.org/W2245493112","https://openalex.org/W2283896980","https://openalex.org/W2301363727","https://openalex.org/W2395121474","https://openalex.org/W2395484564","https://openalex.org/W2522561498","https://openalex.org/W2761405215","https://openalex.org/W2786161686","https://openalex.org/W2797928606","https://openalex.org/W2916932086","https://openalex.org/W2946507061","https://openalex.org/W2967988901","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W3042807565","https://openalex.org/W3083891030","https://openalex.org/W3098219276","https://openalex.org/W3099802519","https://openalex.org/W3110022938","https://openalex.org/W3110247210","https://openalex.org/W3112330479","https://openalex.org/W3115948762","https://openalex.org/W3186145246","https://openalex.org/W3190461479","https://openalex.org/W3206047950","https://openalex.org/W6755182157"],"related_works":["https://openalex.org/W1981780420","https://openalex.org/W2182707996","https://openalex.org/W45233828","https://openalex.org/W2964988449","https://openalex.org/W2397952901","https://openalex.org/W2029380707","https://openalex.org/W4255934811","https://openalex.org/W2465382974","https://openalex.org/W2010229520","https://openalex.org/W2547528905"],"abstract_inverted_index":{"Classification":[0,82],"of":[1,3,9,16,23,30,37,65,83,85,92,103,123,126,128,152],"sequences":[2,91],"temporal":[4,93,129],"intervals":[5],"is":[6,134,151],"a":[7,20,28,73,100,120],"part":[8],"time":[10],"series":[11,15,32],"analysis":[12],"which":[13],"concerns":[14],"events.":[17],"We":[18,34,62,118,141],"propose":[19,119],"new":[21,47,145],"method":[22,75,97],"transforming":[24],"the":[25,38,42,46,55,59,124],"problem":[26,125],"to":[27,49,113],"task":[29],"multivariate":[31],"classification.":[33],"use":[35],"one":[36],"state-of-the-art":[39,56],"algorithms":[40],"from":[41,58],"latter":[43],"domain":[44],"on":[45,90,115],"representation":[48],"obtain":[50],"significantly":[51],"better":[52,107],"accuracy":[53,104],"than":[54],"methods":[57],"former":[60],"field.":[61],"discuss":[63],"limitations":[64],"this":[66,149],"workflow":[67],"and":[68,105],"address":[69],"them":[70],"by":[71],"developing":[72],"novel":[74],"for":[76,81],"classification":[77,127],"termed":[78],"COSTI":[79],"(short":[80],"Sequences":[84],"Temporal":[86],"Intervals)":[87],"operating":[88,114],"directly":[89],"intervals.":[94],"The":[95],"proposed":[96],"remains":[98],"at":[99],"high":[101],"level":[102],"obtains":[106],"performance":[108],"while":[109],"avoiding":[110],"shortcomings":[111],"connected":[112],"transformed":[116],"data.":[117],"generalized":[121],"version":[122],"intervals,":[130],"where":[131,148],"each":[132],"event":[133],"supplemented":[135],"with":[136],"information":[137,150],"about":[138],"its":[139],"intensity.":[140],"also":[142],"provide":[143],"two":[144],"data":[146],"sets":[147],"substantial":[153],"value.":[154]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}