{"id":"https://openalex.org/W2219238525","doi":"https://doi.org/10.1109/bigdata.2015.7364069","title":"Exploring spatio-temporal-theme correlation between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors","display_name":"Exploring spatio-temporal-theme correlation between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2219238525","doi":"https://doi.org/10.1109/bigdata.2015.7364069","mag":"2219238525"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7364069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7364069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","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/A5023083273","display_name":"Minh-Son Dao","orcid":"https://orcid.org/0000-0003-3044-8175"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Minh-Son Dao","raw_affiliation_strings":["National Institute of Information and Communications Technology, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Kyoto, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048072689","display_name":"Koji Zettsu","orcid":"https://orcid.org/0000-0003-4062-2376"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Zettsu","raw_affiliation_strings":["National Institute of Information and Communications Technology, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Kyoto, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060634928","display_name":"Siripen Pongpaichet","orcid":"https://orcid.org/0000-0002-2505-9477"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siripen Pongpaichet","raw_affiliation_strings":["University of California, Irvine, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030483392","display_name":"Laleh Jalali","orcid":null},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laleh Jalali","raw_affiliation_strings":["University of California, Irvine, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010478919","display_name":"Ramesh Jain","orcid":"https://orcid.org/0000-0003-2373-4966"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramesh Jain","raw_affiliation_strings":["University of California, Irvine, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine, USA","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5023083273"],"corresponding_institution_ids":["https://openalex.org/I90023481"],"apc_list":null,"apc_paid":null,"fwci":0.8616,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.74976679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"56","issue":null,"first_page":"2690","last_page":"2699"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976999759674072,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976999759674072,"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":0.9955000281333923,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8093023300170898},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.673982560634613},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5309574007987976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5008411407470703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48970988392829895},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.48172739148139954},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45197343826293945},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44726884365081787},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38989877700805664}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8093023300170898},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.673982560634613},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5309574007987976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5008411407470703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48970988392829895},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.48172739148139954},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45197343826293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44726884365081787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38989877700805664},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7364069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7364069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W151691756","https://openalex.org/W1639702422","https://openalex.org/W1973026225","https://openalex.org/W1989037929","https://openalex.org/W1989807709","https://openalex.org/W1989894105","https://openalex.org/W2003366520","https://openalex.org/W2026302857","https://openalex.org/W2030452017","https://openalex.org/W2041354089","https://openalex.org/W2061887386","https://openalex.org/W2104925568","https://openalex.org/W2115702359","https://openalex.org/W2119821739","https://openalex.org/W2158382689","https://openalex.org/W2616802916","https://openalex.org/W2744796818","https://openalex.org/W2998574808","https://openalex.org/W4239510810","https://openalex.org/W4242702158","https://openalex.org/W4285719527","https://openalex.org/W6636822491","https://openalex.org/W6683424812","https://openalex.org/W6738298939","https://openalex.org/W6742654839"],"related_works":["https://openalex.org/W4386799044","https://openalex.org/W2773208253","https://openalex.org/W2560646951","https://openalex.org/W4297454206","https://openalex.org/W65104662","https://openalex.org/W1871748041","https://openalex.org/W2362286668","https://openalex.org/W2133382151","https://openalex.org/W2153339597","https://openalex.org/W1528412344"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,28],"introduce":[4],"a":[5,30,131,134,213,224],"new":[6,169],"method":[7,78,114,203],"that":[8,46,200],"explores":[9],"spatio-temporal-theme":[10],"correlations":[11],"between":[12],"physical":[13,65],"and":[14,21,40,107,122,181,233],"social":[15,55],"streaming":[16,174],"data":[17,103,120],"for":[18,118,130,191],"event":[19,146,164],"detection":[20],"pattern":[22,35],"interpretation":[23],"from":[24,54,64,96,173],"heterogeneous":[25],"sensors.":[26,66],"Particularly,":[27],"employ":[29],"basic":[31],"two-phase":[32],"framework":[33],"in":[34,108,123,212,226],"recognition":[36],"(i.e.":[37],"feature":[38],"extraction":[39],"detection)":[41],"with":[42,140,171],"the":[43,48,60,70,76,112,141,153,160,163,193,201,207,227],"novel":[44],"improvement":[45],"concerns":[47],"use":[49],"of":[50,73,91,162,178,209],"semantic":[51],"information":[52],"acquired":[53],"sensors":[56],"to":[57,82,86,158],"automatically":[58,121],"label":[59],"low-level":[61],"features":[62],"extracted":[63],"Moreover,":[67,127],"by":[68,166,243],"symbolizing":[69],"trend":[71],"component":[72],"time-series":[74],"data,":[75],"proposed":[77,113,194,202],"has":[79],"an":[80,109,124,145],"ability":[81],"interpret":[83],"event's":[84,246],"patterns":[85],"help":[87,220],"users":[88,221],"get":[89],"insights":[90],"how":[92,223],"events":[93],"happen.":[94],"Differentiating":[95],"conventional":[97],"supervised":[98],"learning":[99,157],"methods":[100],"whose":[101],"training":[102,119,135,154],"are":[104],"labeled":[105],"manually":[106],"off-line":[110],"mode,":[111],"can":[115,137,204,236],"collect":[116],"labels":[117,172],"on-line":[125],"mode.":[126],"after":[128],"running":[129],"certain":[132],"time,":[133],"stage":[136,143,155],"run":[138],"parallel":[139],"detecting":[142],"when":[144],"model":[147,165],"is":[148,189],"totally":[149],"built.":[150],"After":[151],"that,":[152],"continues":[156],"increase":[159],"accuracy":[161],"nonstop":[167],"collecting":[168],"samples":[170],"data.":[175],"The":[176,196],"problem":[177],"environmental":[179],"factors":[180],"particularly":[182],"air":[183,234],"pollution":[184],"impacts":[185],"on":[186],"asthma":[187,210,241],"exacerbation":[188],"considered":[190],"evaluating":[192],"method.":[195],"experimental":[197],"results":[198],"show":[199],"probably":[205],"detect":[206],"prevalence":[208],"risks":[211],"specific":[214],"spatio-temporal":[215],"context":[216],"as":[217,219],"well":[218],"understand":[222],"change":[225],"surrounding":[228],"environment":[229],"(e.g.":[230,240],"weather":[231],"condition":[232],"pollution)":[235],"influence":[237],"their":[238],"health":[239],"attack)":[242],"interpreting":[244],"detected":[245],"patterns.":[247]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
