{"id":"https://openalex.org/W4404986645","doi":"https://doi.org/10.1145/3703323.3703337","title":"A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation","display_name":"A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4404986645","doi":"https://doi.org/10.1145/3703323.3703337"},"language":"en","primary_location":{"id":"doi:10.1145/3703323.3703337","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703337","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3703323.3703337","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114987600","display_name":"Vennela Yarabolu","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vennela Yarabolu","raw_affiliation_strings":["Computer Science, Indian Institute of Technology, Bombay, Mumbai, India"],"raw_orcid":"https://orcid.org/0009-0003-6497-4162","affiliations":[{"raw_affiliation_string":"Computer Science, Indian Institute of Technology, Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009355682","display_name":"Govind Waghmare","orcid":"https://orcid.org/0000-0002-2953-1847"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Govind Waghmare","raw_affiliation_strings":["Mastercard, Gurugram, India"],"raw_orcid":"https://orcid.org/0000-0002-2953-1847","affiliations":[{"raw_affiliation_string":"Mastercard, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054341033","display_name":"Sonia Gupta","orcid":"https://orcid.org/0000-0001-6143-8653"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sonia Gupta","raw_affiliation_strings":["Mastercard, Gurugram, India"],"raw_orcid":"https://orcid.org/0000-0001-6143-8653","affiliations":[{"raw_affiliation_string":"Mastercard, Gurugram, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076286184","display_name":"Siddhartha Asthana","orcid":"https://orcid.org/0000-0002-6798-1240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siddhartha Asthana","raw_affiliation_strings":["Mastercard, Gurugram, India"],"raw_orcid":"https://orcid.org/0000-0002-6798-1240","affiliations":[{"raw_affiliation_string":"Mastercard, Gurugram, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18948957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"84","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9890000224113464,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9843000173568726,"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/computer-science","display_name":"Computer science","score":0.8041335940361023},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.7181448340415955},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7084850668907166},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.577781081199646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5350341200828552},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.4468197524547577},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4411272406578064},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42577823996543884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4141686260700226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3693701922893524},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.19164156913757324},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11541229486465454}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8041335940361023},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.7181448340415955},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7084850668907166},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.577781081199646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5350341200828552},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.4468197524547577},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4411272406578064},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42577823996543884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4141686260700226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3693701922893524},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.19164156913757324},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11541229486465454},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3703323.3703337","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703337","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2411.15616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.15616","pdf_url":"https://arxiv.org/pdf/2411.15616","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3703323.3703337","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703337","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W123476658","https://openalex.org/W1501483081","https://openalex.org/W1525647652","https://openalex.org/W1585854823","https://openalex.org/W1904826605","https://openalex.org/W1968980002","https://openalex.org/W2000454347","https://openalex.org/W2028138594","https://openalex.org/W2034368206","https://openalex.org/W2047449506","https://openalex.org/W2068714596","https://openalex.org/W2085261163","https://openalex.org/W2097079088","https://openalex.org/W2099419573","https://openalex.org/W2120587290","https://openalex.org/W2133088989","https://openalex.org/W2139327121","https://openalex.org/W2143991132","https://openalex.org/W2157103390","https://openalex.org/W2160512933","https://openalex.org/W2244109919","https://openalex.org/W2517990807","https://openalex.org/W2585528949","https://openalex.org/W2588336250","https://openalex.org/W2591909298","https://openalex.org/W2963991843","https://openalex.org/W3081751010","https://openalex.org/W4253930552","https://openalex.org/W4255466416","https://openalex.org/W4393161160"],"related_works":["https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W2990081132","https://openalex.org/W2511384863","https://openalex.org/W2096089271","https://openalex.org/W4296984035","https://openalex.org/W2923628599","https://openalex.org/W2014100433","https://openalex.org/W2051519658","https://openalex.org/W2994787386"],"abstract_inverted_index":{"In":[0],"many":[1],"real-world":[2,223],"applications,":[3],"continuous":[4],"machine":[5],"learning":[6],"(ML)":[7],"systems":[8,258],"are":[9,154],"crucial":[10],"but":[11],"prone":[12],"to":[13,28,70,140,213,254],"data":[14,22,26,84,93,137,143,149,152,178,210],"drift\u2014a":[15],"phenomenon":[16],"where":[17],"discrepancies":[18],"between":[19],"historical":[20,48,83],"training":[21,97,158],"and":[23,32,50,77,103,124,130,168,180,190,202,207,224,237,251],"future":[24],"test":[25,148,160],"lead":[27],"significant":[29,214],"performance":[30],"degradation":[31],"operational":[33,191,235],"inefficiencies.":[34],"Traditional":[35],"drift":[36,56,126,182],"adaptation":[37],"methods":[38,61],"typically":[39],"update":[40],"models":[41,165],"using":[42],"ensemble":[43],"techniques,":[44],"often":[45],"discarding":[46],"drifted":[47,92],"data,":[49,161],"focus":[51],"primarily":[52],"on":[53,159,219,222],"either":[54],"covariate":[55,123],"or":[57],"concept":[58,125],"drift.":[59],"These":[60,151],"face":[62],"issues":[63],"such":[64],"as":[65],"high":[66],"resource":[67,200],"demands,":[68],"inability":[69],"manage":[71],"all":[72],"types":[73],"of":[74,115,121,176],"drifts":[75],"effectively,":[76],"neglecting":[78],"the":[79,95,113,164,174],"valuable":[80],"context":[81],"that":[82,89,111,145,163],"can":[85],"provide.":[86],"We":[87],"contend":[88],"explicitly":[90],"incorporating":[91],"into":[94],"model":[96,101,188,231],"process":[98],"significantly":[99],"enhances":[100],"accuracy":[102,189,232],"robustness.":[104],"This":[105,239],"paper":[106],"introduces":[107],"an":[108],"advanced":[109],"framework":[110,134],"integrates":[112],"strengths":[114],"data-centric":[116],"approaches":[117],"with":[118],"adaptive":[119],"management":[120],"both":[122,177],"in":[127,193,244],"a":[128,248],"scalable":[129,181,252],"efficient":[131],"manner.":[132],"Our":[133],"employs":[135],"sophisticated":[136],"segmentation":[138,179],"techniques":[139],"identify":[141],"optimal":[142],"batches":[144,153],"accurately":[146],"reflect":[147],"patterns.":[150],"then":[155],"utilized":[156],"for":[157],"ensuring":[162],"remain":[166],"relevant":[167,209],"accurate":[169],"over":[170],"time.":[171],"By":[172],"leveraging":[173],"advantages":[175],"management,":[183],"our":[184,228],"solution":[185,241],"ensures":[186],"robust":[187],"efficiency":[192],"large-scale":[194],"ML":[195,257],"deployments.":[196],"It":[197],"also":[198],"minimizes":[199],"consumption":[201],"computational":[203],"overhead":[204],"by":[205],"selecting":[206],"utilizing":[208],"subsets,":[211],"leading":[212],"cost":[215],"savings.":[216],"Experimental":[217],"results":[218],"classification":[220],"task":[221],"synthetic":[225],"datasets":[226],"show":[227],"approach":[229,253],"improves":[230],"while":[233],"reducing":[234],"costs":[236],"latency.":[238],"practical":[240],"overcomes":[242],"inefficiencies":[243],"current":[245],"methods,":[246],"providing":[247],"robust,":[249],"adaptable,":[250],"maintaining":[255],"high-performance":[256],"across":[259],"various":[260],"applications.":[261]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2024-12-04T00:00:00"}