{"id":"https://openalex.org/W2102181539","doi":"https://doi.org/10.1145/1791212.1791278","title":"Discovering routine events in sensor streams for macroscopic sensing composition","display_name":"Discovering routine events in sensor streams for macroscopic sensing composition","publication_year":2010,"publication_date":"2010-04-12","ids":{"openalex":"https://openalex.org/W2102181539","doi":"https://doi.org/10.1145/1791212.1791278","mag":"2102181539"},"language":"en","primary_location":{"id":"doi:10.1145/1791212.1791278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1791212.1791278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks","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/A5041802292","display_name":"Athanasios Bamis","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Athanasios Bamis","raw_affiliation_strings":["Yale University","Yale University ;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University ;","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101562545","display_name":"Jia Fang","orcid":"https://orcid.org/0000-0001-8241-209X"},"institutions":[{"id":"https://openalex.org/I919571938","display_name":"The University of Texas Health Science Center at Houston","ror":"https://ror.org/03gds6c39","country_code":"US","type":"education","lineage":["https://openalex.org/I919571938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jia Fang","raw_affiliation_strings":["University of Texas Health Science Center at Houston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas Health Science Center at Houston","institution_ids":["https://openalex.org/I919571938"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027175650","display_name":"Andreas Savvides","orcid":"https://orcid.org/0000-0002-0759-1275"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Savvides","raw_affiliation_strings":["Yale University","Yale University ;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University ;","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.17623378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"408","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9758999943733215,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9745000004768372,"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/testbed","display_name":"Testbed","score":0.8676807880401611},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.786959707736969},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.7251142263412476},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6625065207481384},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.6455861330032349},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5925125479698181},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5005393028259277},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.44337233901023865},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.365854412317276},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3456389307975769},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10192903876304626}],"concepts":[{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.8676807880401611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786959707736969},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.7251142263412476},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6625065207481384},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.6455861330032349},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5925125479698181},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5005393028259277},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.44337233901023865},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.365854412317276},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3456389307975769},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10192903876304626},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1791212.1791278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1791212.1791278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1861465509","https://openalex.org/W2018804066","https://openalex.org/W2108072252","https://openalex.org/W2144621085"],"related_works":["https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W3003320923","https://openalex.org/W2106140982","https://openalex.org/W2152313554","https://openalex.org/W2064303750","https://openalex.org/W1509300825","https://openalex.org/W3092582874","https://openalex.org/W2338718585","https://openalex.org/W2054620577"],"abstract_inverted_index":{"This":[0],"poster":[1],"abstract":[2],"introduces":[3],"the":[4,37,44,69,72,75,80,103,108,115,127,158],"problem":[5],"of":[6,24,39,46,53,114,149,157],"macroscopic":[7],"sensing":[8],"composition,":[9],"where":[10,102],"a":[11,22,28,50,99],"sensor":[12],"capable":[13],"to":[14,61,118],"detect":[15],"complex":[16],"events":[17,120,154],"is":[18,33,60,105,110,117],"synthesized":[19],"dynamically":[20],"by":[21,42,67,151],"collection":[23],"simpler":[25],"sensors":[26,48],"using":[27,137],"data-driven":[29],"approach.":[30],"Our":[31,134],"solution":[32],"geared":[34],"towards":[35],"discovering":[36],"structure":[38],"human":[40],"activities":[41],"considering":[43],"triggering":[45],"simple":[47],"over":[49],"diverse":[51],"set":[52],"spatial":[54],"and":[55,124,140],"temporal":[56,77,163],"scales.":[57],"The":[58,112],"goal":[59,113],"identify":[62,119],"routines":[63,81,150],"from":[64],"their":[65,162],"components":[66,73,148],"leveraging":[68],"fact":[70],"that":[71,143],"have":[74,87],"same":[76,159],"persistence":[78],"as":[79],"themselves.":[82],"To":[83],"this":[84,122,144],"end":[85],"we":[86],"devised":[88],"an":[89,94],"algorithm":[90,116],"for":[91],"determining":[92],"if":[93],"event":[95,109],"occurs":[96],"consistently":[97],"within":[98],"time":[100],"interval":[101,104,129],"periodic":[106],"but":[107],"not.":[111],"with":[121],"property":[123],"also":[125],"determine":[126],"minimum":[128],"in":[130],"which":[131,153],"they":[132],"occur.":[133],"first":[135],"results":[136],"testbed":[138],"data":[139],"simulations":[141],"indicate":[142],"approach":[145],"can":[146],"uncover":[147],"identifying":[152],"are":[155],"parts":[156],"routine":[160],"through":[161],"properties.":[164]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
