{"id":"https://openalex.org/W1565871507","doi":"https://doi.org/10.1145/2757001.2757006","title":"Ensemble stream model for data-cleaning in sensor networks","display_name":"Ensemble stream model for data-cleaning in sensor networks","publication_year":2015,"publication_date":"2015-06-16","ids":{"openalex":"https://openalex.org/W1565871507","doi":"https://doi.org/10.1145/2757001.2757006","mag":"1565871507"},"language":"en","primary_location":{"id":"doi:10.1145/2757001.2757006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2757001.2757006","pdf_url":null,"source":{"id":"https://openalex.org/S4210187303","display_name":"AI Matters","issn_l":"2372-3483","issn":["2372-3483"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AI Matters","raw_type":"journal-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/A5022570209","display_name":"Vasanth Iyer","orcid":"https://orcid.org/0000-0002-1909-4276"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vasanth Iyer","raw_affiliation_strings":["Florida International University","FLORIDA INTERNATIONAL UNIVERSITY"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"FLORIDA INTERNATIONAL UNIVERSITY","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102765260","display_name":"S. S. Iyengar","orcid":"https://orcid.org/0000-0003-3203-833X"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. S. Iyengar","raw_affiliation_strings":["Florida International University","FLORIDA INTERNATIONAL UNIVERSITY"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"FLORIDA INTERNATIONAL UNIVERSITY","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111906965","display_name":"Niki Pissinou","orcid":null},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Niki Pissinou","raw_affiliation_strings":["Florida International University","FLORIDA INTERNATIONAL UNIVERSITY"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"FLORIDA INTERNATIONAL UNIVERSITY","institution_ids":["https://openalex.org/I19700959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022570209"],"corresponding_institution_ids":["https://openalex.org/I19700959"],"apc_list":null,"apc_paid":null,"fwci":0.6997,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7389355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"1","issue":"4","first_page":"29","last_page":"32"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9674999713897705,"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/T10320","display_name":"Neural Networks and Applications","score":0.9668999910354614,"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":0.5429626703262329}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5429626703262329}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2757001.2757006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2757001.2757006","pdf_url":null,"source":{"id":"https://openalex.org/S4210187303","display_name":"AI Matters","issn_l":"2372-3483","issn":["2372-3483"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AI Matters","raw_type":"journal-article"},{"id":"pmh:oai:digitalcommons.fiu.edu:dissertations-2815","is_oa":false,"landing_page_url":"https://digitalcommons.fiu.edu/dissertations/AAI3608724","pdf_url":null,"source":{"id":"https://openalex.org/S4306401637","display_name":"Florida International University Digital Commons (Florida International University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I19700959","host_organization_name":"Florida International University","host_organization_lineage":["https://openalex.org/I19700959"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ProQuest ETD Collection for FIU","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W198267595","https://openalex.org/W1970571562","https://openalex.org/W1980773789","https://openalex.org/W2038713122","https://openalex.org/W2099829548","https://openalex.org/W2120512335","https://openalex.org/W2125086691","https://openalex.org/W2158981872","https://openalex.org/W2472323202","https://openalex.org/W2911964244","https://openalex.org/W2973651390","https://openalex.org/W3120740533","https://openalex.org/W6720398448"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Ensemble":[0,278],"Stream":[1],"Modeling":[2],"and":[3,13,58,199,233,299,311,335],"Data-cleaning":[4],"are":[5,20,56,144,150,166],"sensor":[6,164,205,316],"information":[7,249],"processing":[8,44],"systems":[9],"have":[10],"different":[11],"training":[12,224],"testing":[14],"methods":[15],"by":[16,33,78,214],"which":[17,27,86,230],"their":[18],"goals":[19],"cross-validated.":[21],"This":[22],"research":[23],"examines":[24],"a":[25,109,290],"mechanism,":[26],"seeks":[28],"to":[29,46,51,103,181,222,235,255,273,283,302,318],"extract":[30],"novel":[31],"patterns":[32],"generating":[34],"ensembles":[35],"from":[36,132,204],"data.":[37],"The":[38,91,146,244,294],"main":[39],"goal":[40],"of":[41,119,155,211,241,251,305,315,321,331,342],"label-less":[42],"stream":[43,279,332],"is":[45,96,137,158,196,202,208,267],"process":[47,210],"the":[48,53,60,88,94,120,142,153,162,190,193,209,237,252,275,300,319,329],"sensed":[49,124],"events":[50],"eliminate":[52,189],"noises":[54],"that":[55],"uncorrelated,":[57],"choose":[59],"most":[61],"likely":[62],"model":[63,70,140,280],"without":[64],"over":[65],"fitting":[66],"thus":[67],"obtaining":[68],"higher":[69,257],"confidence.":[71],"Higher":[72],"quality":[73,304,333],"streams":[74,82,322,345],"can":[75],"be":[76],"realized":[77],"combining":[79],"many":[80],"short":[81],"into":[83],"an":[84,97,117,138],"ensemble":[85,295],"has":[87],"desired":[89],"quality.":[90],"framework":[92,296],"for":[93,148,297,323],"investigation":[95],"existing":[98],"data":[99],"mining":[100],"tool.":[101],"First,":[102],"accommodate":[104],"feature":[105,212],"extraction":[106],"such":[107,264],"as":[108,127,265],"bush":[110],"or":[111,219],"natural":[112],"forest-fire":[113],"event":[114],"we":[115,179,187],"make":[116],"assumption":[118],"burnt":[121],"area":[122],"(BA*),":[123],"ground":[125],"truth":[126],"our":[128],"target":[129,253],"variable":[130],"obtained":[131],"logs.":[133],"Even":[134],"though":[135],"this":[136,149],"obvious":[139],"choice":[141],"results":[143],"disappointing.":[145],"reasons":[147],"two:":[151],"One,":[152],"histogram":[154,195],"fire":[156,242],"activity":[157],"highly":[159,167],"skewed.":[160],"Two,":[161],"measured":[163],"parameters":[165],"correlated.":[168],"Since":[169],"using":[170,286],"non":[171],"descriptive":[172],"features":[173,288],"does":[174],"not":[175],"yield":[176],"good":[177],"results,":[178],"resort":[180],"temporal":[182],"features.":[183,259],"By":[184],"doing":[185],"so":[186],"carefully":[188],"averaging":[191],"effects;":[192],"resulting":[194],"more":[197],"satisfactory":[198],"conceptual":[200],"knowledge":[201],"learned":[203],"streams.":[206],"Second":[207],"induction":[213],"cross-validating":[215],"attributes":[216],"with":[217,289],"single":[218],"multi-target":[220,245],"variables":[221],"minimize":[223],"error.":[225],"We":[226],"use":[227,248],"F-measure":[228],"score,":[229],"combines":[231],"precision":[232],"accuracy":[234],"determine":[236],"false":[238],"alarm":[239],"rate":[240],"events.":[243],"data-cleaning":[246,298],"trees":[247],"purity":[250],"leaf-nodes":[254],"learn":[256],"order":[258],"A":[260],"sensitive":[261],"variance":[262],"measure":[263],"\u0192-test":[266],"performed":[268],"during":[269],"each":[270],"node's":[271],"split":[272],"select":[274],"best":[276],"attribute.":[277],"approach":[281],"proved":[282],"improve":[284],"when":[285],"complicated":[287],"simpler":[291],"tree":[292],"classifier.":[293],"enhancements":[301],"quantify":[303],"fitness":[306],"(30%":[307],"spatial,":[308],"10%":[309],"temporal,":[310],"90%":[312],"mobility":[313],"reduction)":[314],"led":[317],"formation":[320],"sensor-enabled":[324],"applications.":[325],"Which":[326],"further":[327],"motivates":[328],"novelty":[330],"labeling":[334],"its":[336],"importance":[337],"in":[338],"solving":[339],"vast":[340],"amounts":[341],"real-time":[343],"mobile":[344],"generated":[346],"today.":[347]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}