{"id":"https://openalex.org/W2991594330","doi":"https://doi.org/10.1109/smc.2019.8914227","title":"Cloud-assisted Adaptive Stream Processing from Discriminative Representations","display_name":"Cloud-assisted Adaptive Stream Processing from Discriminative Representations","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2991594330","doi":"https://doi.org/10.1109/smc.2019.8914227","mag":"2991594330"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2019.8914227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109296416","display_name":"Maryleen Ndubuaku","orcid":null},"institutions":[{"id":"https://openalex.org/I22128151","display_name":"University of Derby","ror":"https://ror.org/02yhrrk59","country_code":"GB","type":"education","lineage":["https://openalex.org/I22128151"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Maryleen Ndubuaku","raw_affiliation_strings":["University of Derby, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Derby, UK","institution_ids":["https://openalex.org/I22128151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032843445","display_name":"Ashiq Anjum","orcid":"https://orcid.org/0000-0002-3378-1152"},"institutions":[{"id":"https://openalex.org/I22128151","display_name":"University of Derby","ror":"https://ror.org/02yhrrk59","country_code":"GB","type":"education","lineage":["https://openalex.org/I22128151"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ashiq Anjum","raw_affiliation_strings":["University of Derby, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Derby, UK","institution_ids":["https://openalex.org/I22128151"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026941307","display_name":"Antonio Liotta","orcid":"https://orcid.org/0000-0002-2773-4421"},"institutions":[{"id":"https://openalex.org/I251738","display_name":"Edinburgh Napier University","ror":"https://ror.org/03zjvnn91","country_code":"GB","type":"education","lineage":["https://openalex.org/I251738"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Antonio Liotta","raw_affiliation_strings":["Edinburgh Napier University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Edinburgh Napier University, UK","institution_ids":["https://openalex.org/I251738"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"abs 1802 187","issue":null,"first_page":"164","last_page":"169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9972000122070312,"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":0.9965000152587891,"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":0.8344024419784546},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7517218589782715},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.720086932182312},{"id":"https://openalex.org/keywords/stream-processing","display_name":"Stream processing","score":0.5690934658050537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5332291722297668},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5276901721954346},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5236794352531433},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5231146216392517},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.4516918659210205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4269815683364868},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41930216550827026},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41647446155548096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4106745719909668},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.1890549659729004}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8344024419784546},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7517218589782715},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.720086932182312},{"id":"https://openalex.org/C107027933","wikidata":"https://www.wikidata.org/wiki/Q2006448","display_name":"Stream processing","level":2,"score":0.5690934658050537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5332291722297668},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5276901721954346},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5236794352531433},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5231146216392517},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.4516918659210205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4269815683364868},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41930216550827026},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41647446155548096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4106745719909668},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.1890549659729004},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc.2019.8914227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:napier-surface.worktribe.com:2656092","is_oa":false,"landing_page_url":"http://researchrepository.napier.ac.uk/Output/2656092","pdf_url":null,"source":{"id":"https://openalex.org/S4306400544","display_name":"Research Output (Edinburgh Napier University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I251738","host_organization_name":"Edinburgh Napier University","host_organization_lineage":["https://openalex.org/I251738"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W126044619","https://openalex.org/W1522301498","https://openalex.org/W1646619842","https://openalex.org/W1965972569","https://openalex.org/W1969082358","https://openalex.org/W2094547360","https://openalex.org/W2112796928","https://openalex.org/W2119939946","https://openalex.org/W2125800352","https://openalex.org/W2127218421","https://openalex.org/W2134017119","https://openalex.org/W2187089797","https://openalex.org/W2204904589","https://openalex.org/W2533545350","https://openalex.org/W2579718262","https://openalex.org/W2608862709","https://openalex.org/W2741943936","https://openalex.org/W2750384547","https://openalex.org/W2765741717","https://openalex.org/W2768718523","https://openalex.org/W2801742018","https://openalex.org/W2912754914","https://openalex.org/W2913341171","https://openalex.org/W2962849408","https://openalex.org/W2962852342","https://openalex.org/W2964074409","https://openalex.org/W2964121744","https://openalex.org/W3153872861","https://openalex.org/W4214958790","https://openalex.org/W6605131723","https://openalex.org/W6631190155","https://openalex.org/W6641538317","https://openalex.org/W6678914141","https://openalex.org/W6685380521","https://openalex.org/W6743688258","https://openalex.org/W6748713143","https://openalex.org/W6758990916"],"related_works":["https://openalex.org/W2082671441","https://openalex.org/W2605169415","https://openalex.org/W180351855","https://openalex.org/W2538312128","https://openalex.org/W2904574017","https://openalex.org/W3113817755","https://openalex.org/W2794178276","https://openalex.org/W2295837926","https://openalex.org/W4234804492","https://openalex.org/W2529735807"],"abstract_inverted_index":{"As":[0],"the":[1,31,34,42,53,57,91,95,118,122,133,156,169,188],"streaming":[2,92],"data":[3,93,96,149],"generated":[4],"by":[5],"Internet":[6],"of":[7,90,171],"Things":[8],"(IoT)":[9],"ubiquitous":[10],"sensors":[11],"grow":[12],"in":[13,20,33,41,126,139],"massive":[14],"scale,":[15],"extracting":[16],"interesting":[17],"information":[18],"(anomalies)":[19],"real-time":[21,39,45],"becomes":[22],"more":[23],"challenging.":[24],"Traditional":[25],"systems":[26],"which":[27,47],"retrospectively":[28],"perform":[29],"all":[30],"processing":[32],"cloud":[35,123],"do":[36],"not":[37],"capture":[38],"changes":[40],"data.":[43],"Similarly,":[44],"solutions":[46],"rely":[48],"on":[49,79,132,204],"human":[50],"monitors":[51],"have":[52,71,162],"tendency":[54],"to":[55,60,85,87,124,137,143,154,180],"miss":[56],"anomalies":[58],"due":[59],"their":[61],"rare":[62],"nature.":[63],"In":[64,158],"recent":[65],"times,":[66],"several":[67],"machine":[68],"learning":[69,83],"techniques":[70],"been":[72],"proposed":[73,163,189],"for":[74],"stream":[75,127],"processing.":[76,128],"Approaches":[77],"based":[78],"supervised":[80],"or":[81],"semi-supervised":[82],"fail":[84],"adapt":[86],"changing":[88],"patterns":[89],"and":[94,121,147,174,199],"labelling":[97],"costs":[98],"are":[99],"huge.":[100],"To":[101],"address":[102],"these":[103],"limitations,":[104],"we":[105,161],"propose":[106],"a":[107],"cloud-assisted":[108],"framework":[109],"where":[110],"an":[111,140,164],"intermediary":[112],"node":[113],"(edge)":[114],"is":[115,135],"introduced":[116],"between":[117,145],"end":[119],"devices":[120],"assist":[125],"A":[129],"model":[130],"deployed":[131],"edge":[134],"designed":[136],"learn":[138],"iterative":[141,165],"manner":[142],"discriminate":[144],"similar":[146],"dissimilar":[148],"representations,":[150],"making":[151],"it":[152],"easier":[153],"distinguish":[155],"anomalies.":[157],"this":[159],"work,":[160],"method":[166,190],"that":[167],"combines":[168],"capabilities":[170],"deep":[172],"clustering":[173],"l":[175],"<sub":[176],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[177],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[178],"-normalisation":[179],"achieve":[181],"better":[182],"discriminative":[183,196],"representations.":[184],"Experimental":[185],"results":[186],"demonstrate":[187],"achieves":[191],"robust":[192],"performance":[193],"over":[194],"state-of-the-art":[195],"representation":[197],"algorithms":[198],"sets":[200],"new":[201],"benchmark":[202],"accuracy":[203],"transformation":[205],"invariant":[206],"image":[207],"dataset.":[208]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}