{"id":"https://openalex.org/W7135181655","doi":"https://doi.org/10.48550/arxiv.2603.11094","title":"Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information","display_name":"Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135181655","doi":"https://doi.org/10.48550/arxiv.2603.11094"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11094","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.11094","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129017676","display_name":"Ben Halstead","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Halstead, Ben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017570709","display_name":"Yun Sing Koh","orcid":"https://orcid.org/0000-0001-7256-4049"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koh, Yun Sing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028387093","display_name":"Patricia Riddle","orcid":"https://orcid.org/0000-0001-8616-0053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riddle, Patricia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107544599","display_name":"Mykola Pechenizkiy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pechenizkiy, Mykola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121070852","display_name":"Albert Bifet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bifet, Albert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081515766","display_name":"Russel Pears","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pears, Russel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9958000183105469,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9958000183105469,"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/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0003000000142492354,"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/concept-drift","display_name":"Concept drift","score":0.7588000297546387},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6093000173568726},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5843999981880188},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.525600016117096},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5164999961853027},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.46799999475479126},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4580000042915344},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7821999788284302},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.7588000297546387},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6093000173568726},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5843999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5756000280380249},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5009999871253967},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46799999475479126},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11094","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11094","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4631008207798004,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Streaming":[0],"sources":[1],"of":[2,33,65,72,97,111,139,147,177,192],"data":[3,13,23,34],"are":[4,89],"becoming":[5],"more":[6,156],"common":[7],"as":[8],"the":[9,31,63],"ability":[10],"to":[11,40,58,69,79,123,132,153,180],"collect":[12],"in":[14,20,30,42,56,142,169,199],"real-time":[15],"grows.":[16],"A":[17],"major":[18],"concern":[19],"dealing":[21],"with":[22],"streams":[24],"is":[25,52],"concept":[26,59,67,77,98,105,124,141,167,205],"drift,":[27],"a":[28,53,66,70,80,129,140,143,145,170,174,190],"change":[29],"distribution":[32],"over":[35,189],"time,":[36],"for":[37],"example,":[38],"due":[39],"changes":[41],"environmental":[43],"conditions.":[44],"Representing":[45],"concepts":[46],"(stationary":[47],"periods":[48],"featuring":[49],"similar":[50],"behaviour)":[51],"key":[54],"idea":[55],"adapting":[57],"drift.":[60,125,206],"By":[61],"testing":[62],"similarity":[64],"representation":[68],"window":[71],"observations,":[73],"we":[74],"can":[75],"detect":[76],"drift":[78,168],"new":[81],"or":[82],"previously":[83,103],"seen":[84],"recurring":[85],"concept.":[86],"Concept":[87],"representations":[88,106,115],"constructed":[90],"using":[91],"meta-information":[92,112,150,164,178],"features,":[93],"values":[94],"describing":[95],"aspects":[96],"behaviour.":[99],"We":[100,126],"find":[101],"that":[102],"proposed":[104],"rely":[107],"on":[108],"small":[109],"numbers":[110],"features.":[113],"These":[114],"often":[116],"cannot":[117],"distinguish":[118],"concepts,":[119],"leaving":[120],"systems":[121],"vulnerable":[122],"propose":[127],"FiCSUM,":[128],"general":[130],"framework":[131],"represent":[133],"both":[134,200],"supervised":[135],"and":[136,196,202],"unsupervised":[137],"behaviours":[138],"fingerprint,":[144],"vector":[146],"many":[148],"distinct":[149],"features":[151,165,179],"able":[152],"uniquely":[154],"identify":[155],"concepts.":[157],"Our":[158],"dynamic":[159],"weighting":[160],"strategy":[161],"learns":[162],"which":[163],"describe":[166],"given":[171],"dataset,":[172],"allowing":[173],"diverse":[175],"set":[176],"be":[181],"used":[182],"at":[183],"once.":[184],"FiCSUM":[185],"outperforms":[186],"state-of-the-art":[187],"methods":[188],"range":[191],"11":[193],"real":[194],"world":[195],"synthetic":[197],"datasets":[198],"accuracy":[201],"modeling":[203],"underlying":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-14T00:00:00"}
