{"id":"https://openalex.org/W2952802156","doi":"https://doi.org/10.1145/3328905.3329510","title":"ASSED","display_name":"ASSED","publication_year":2019,"publication_date":"2019-06-18","ids":{"openalex":"https://openalex.org/W2952802156","doi":"https://doi.org/10.1145/3328905.3329510","mag":"2952802156"},"language":"en","primary_location":{"id":"doi:10.1145/3328905.3329510","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3328905.3329510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.07596","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Abhijit Suprem","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Abhijit Suprem","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":null,"display_name":"Calton Pu","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Calton Pu","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.7233,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7835542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9961000084877014,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.994700014591217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5723000168800354},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/complex-event-processing","display_name":"Complex event processing","score":0.5056999921798706},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.3921999931335449},{"id":"https://openalex.org/keywords/event-monitoring","display_name":"Event monitoring","score":0.3296000063419342},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.3248000144958496},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.32440000772476196},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.3237000107765198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503999829292297},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.5056999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4049000144004822},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33390000462532043},{"id":"https://openalex.org/C2778135862","wikidata":"https://www.wikidata.org/wiki/Q5416719","display_name":"Event monitoring","level":3,"score":0.3296000063419342},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3328905.3329510","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3328905.3329510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.07596","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.07596","pdf_url":"https://arxiv.org/pdf/1909.07596","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.07596","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.07596","pdf_url":"https://arxiv.org/pdf/1909.07596","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W10004740","https://openalex.org/W1585854823","https://openalex.org/W1873218251","https://openalex.org/W1932870546","https://openalex.org/W1973803644","https://openalex.org/W1975632959","https://openalex.org/W1979839410","https://openalex.org/W2053968437","https://openalex.org/W2077550371","https://openalex.org/W2084591134","https://openalex.org/W2095345875","https://openalex.org/W2096765155","https://openalex.org/W2099419573","https://openalex.org/W2120779048","https://openalex.org/W2124499489","https://openalex.org/W2130094219","https://openalex.org/W2141413155","https://openalex.org/W2145065594","https://openalex.org/W2149510050","https://openalex.org/W2151225423","https://openalex.org/W2516581207","https://openalex.org/W2626498001","https://openalex.org/W2747288535","https://openalex.org/W2750351539","https://openalex.org/W2770723401","https://openalex.org/W2771233945","https://openalex.org/W2789731619","https://openalex.org/W2792053130","https://openalex.org/W2828486017","https://openalex.org/W2892623328"],"related_works":[],"abstract_inverted_index":{"Physical":[0],"event":[1,10,128,141,195,251],"detection":[2,142,196,219,252],"has":[3],"long":[4],"been":[5],"the":[6,80,92,117,235],"domain":[7],"of":[8,31,40,58,62,79,237,254],"static":[9,104,229,264],"processors":[11],"operating":[12],"on":[13,143],"numeric":[14],"sensor":[15,45,194],"data.":[16],"This":[17],"works":[18],"well":[19],"for":[20,37,188,263],"large":[21],"scale":[22],"strong-signal":[23],"events":[24,32],"such":[25,33],"as":[26,34],"hurricanes,":[27],"and":[28,50,99,131,138,145,151,164,176,179,201,246],"important":[29],"classes":[30],"earthquakes.":[35],"However,":[36],"a":[38,96,157,217],"variety":[39],"domains":[41],"there":[42],"is":[43,88,156],"insufficient":[44],"coverage,":[46],"e.g.,":[47],"landslides,":[48],"wildfires,":[49],"flooding.":[51],"Social":[52,120],"networks":[53],"have":[54],"provided":[55],"massive":[56],"volume":[57],"data":[59,65,171,244],"from":[60,66,172],"billions":[61],"users,":[63],"but":[64],"these":[67],"generic":[68],"social":[69,86,180,193],"sensors":[70,87],"contain":[71],"much":[72],"more":[73,225],"noise":[74],"than":[75],"physical":[76,140],"sensors.":[77],"One":[78],"most":[81],"difficult":[82],"challenges":[83],"presented":[84],"by":[85,207],"concept":[89,238],"drift,":[90],"where":[91],"terms":[93],"associated":[94],"with":[95,125,147,166],"phenomenon":[97],"evolve":[98],"change":[100],"over":[101],"time,":[102],"rendering":[103],"machine":[105,167],"learning":[106,168],"(ML)":[107],"classifiers":[108],"less":[109],"effective.":[110],"To":[111],"address":[112],"this":[113],"problem,":[114],"we":[115],"develop":[116],"ASSED":[118,155,184,214,233,249],"(Adaptive":[119],"Sensor":[121],"Event":[122],"Detection)":[123],"framework":[124,158],"an":[126],"ML-based":[127],"processing":[129],"engine":[130],"show":[132],"how":[133],"it":[134],"can":[135],"perform":[136,203],"simple":[137],"complex":[139],"strong-":[144],"weak-signal":[146],"low-latency,":[148],"high":[149],"scalability,":[150],"accurate":[152],"coverage.":[153],"Specifically,":[154],"to":[159,185,197,202,228,261],"support":[160,186],"continuous":[161],"filter":[162,205],"generation":[163],"updates":[165],"using":[169],"streaming":[170],"high-confidence":[173,190],"sources":[174,191],"(physical":[175],"annotated":[177],"sensors)":[178],"networks.":[181],"We":[182,212],"build":[183],"procedures":[187],"integrating":[189],"into":[192],"generate":[198],"high-quality":[199],"filters":[200],"dynamic":[204],"selection":[206],"tracking":[208],"its":[209],"own":[210],"performance.":[211],"demonstrate":[213],"capabilities":[215],"through":[216],"landslide":[218],"application":[220],"that":[221],"detects":[222],"almost":[223],"350%":[224],"landslides":[226],"compared":[227,260],"approaches.":[230,265],"More":[231],"importantly,":[232],"automates":[234],"handling":[236],"drift:":[239],"four":[240],"years":[241],"after":[242],"initial":[243],"collection":[245],"classifier":[247],"training,":[248],"achieves":[250],"accuracy":[253],"0.988":[255],"(without":[256],"expert":[257],"manual":[258],"intervention),":[259],"0.762":[262]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-06-27T00:00:00"}
