{"id":"https://openalex.org/W1946632051","doi":"https://doi.org/10.1109/vizsec.2015.7312770","title":"Discovery of rating fraud with real-time streaming visual analytics","display_name":"Discovery of rating fraud with real-time streaming visual analytics","publication_year":2015,"publication_date":"2015-10-25","ids":{"openalex":"https://openalex.org/W1946632051","doi":"https://doi.org/10.1109/vizsec.2015.7312770","mag":"1946632051"},"language":"en","primary_location":{"id":"doi:10.1109/vizsec.2015.7312770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vizsec.2015.7312770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Symposium on Visualization for Cyber Security (VizSec)","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/A5036702239","display_name":"Kodzo Webga","orcid":null},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kodzo Webga","raw_affiliation_strings":["University of North Carolina, Charlotte","University of North Carolina at Charlotte*"],"affiliations":[{"raw_affiliation_string":"University of North Carolina, Charlotte","institution_ids":["https://openalex.org/I102149020"]},{"raw_affiliation_string":"University of North Carolina at Charlotte*","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015271163","display_name":"Aidong Lu","orcid":"https://orcid.org/0000-0002-7684-4512"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aidong Lu","raw_affiliation_strings":["University of North Carolina, Charlotte","University of North Carolina at Charlotte*"],"affiliations":[{"raw_affiliation_string":"University of North Carolina, Charlotte","institution_ids":["https://openalex.org/I102149020"]},{"raw_affiliation_string":"University of North Carolina at Charlotte*","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036702239"],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":null,"apc_paid":null,"fwci":0.7364,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.78377609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9983999729156494,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9983999729156494,"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/T11644","display_name":"Spam and Phishing Detection","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9966999888420105,"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":0.832403838634491},{"id":"https://openalex.org/keywords/visual-analytics","display_name":"Visual analytics","score":0.6342188119888306},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5993646383285522},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5908485651016235},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47859999537467957},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.20631161332130432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.832403838634491},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.6342188119888306},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5993646383285522},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5908485651016235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47859999537467957},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.20631161332130432}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vizsec.2015.7312770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vizsec.2015.7312770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Symposium on Visualization for Cyber Security (VizSec)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.800000011920929,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W13080672","https://openalex.org/W144928601","https://openalex.org/W1946320230","https://openalex.org/W1982983730","https://openalex.org/W1983438351","https://openalex.org/W1999222617","https://openalex.org/W2009109712","https://openalex.org/W2039163945","https://openalex.org/W2053210261","https://openalex.org/W2061459389","https://openalex.org/W2063428639","https://openalex.org/W2087685683","https://openalex.org/W2090447925","https://openalex.org/W2093230809","https://openalex.org/W2094909208","https://openalex.org/W2100738695","https://openalex.org/W2111432262","https://openalex.org/W2113372262","https://openalex.org/W2132068130","https://openalex.org/W2136710010","https://openalex.org/W2154249783","https://openalex.org/W2160631243","https://openalex.org/W2163537852","https://openalex.org/W2166953159","https://openalex.org/W2231925225","https://openalex.org/W2293398899","https://openalex.org/W2530695122","https://openalex.org/W4214673677","https://openalex.org/W6728439129"],"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/W2062940763","https://openalex.org/W2937343495","https://openalex.org/W4360833258"],"abstract_inverted_index":{"The":[0,22],"rating":[1,26,50,87,165],"fraud":[2,160],"in":[3,35,44],"online":[4,49],"e-commerce":[5],"stores":[6],"targets":[7],"at":[8],"receiving":[9],"large":[10,37],"revenues":[11],"through":[12,113,136],"boosting":[13],"the":[14,84,103,121,131,148],"popularity":[15],"of":[16,24,39,62,86,130,158],"selected":[17],"items":[18],"with":[19,155],"fake":[20],"ratings.":[21],"challenges":[23],"detecting":[25,42],"frauds":[27,43,166],"come":[28],"from":[29,48],"discovering":[30],"small":[31],"scale":[32],"abnormal":[33],"activities":[34],"a":[36,45,55,66,74,91,137],"amount":[38],"data":[40,71,112],"and":[41,73,99,105,123,147,162],"time-critical":[46],"manner":[47],"streams.":[51],"This":[52],"paper":[53],"presents":[54],"real-time":[56],"visual":[57,75],"analytics":[58,76],"system":[59,110],"that":[60,164],"consists":[61],"two":[63],"essential":[64],"components:":[65],"server":[67,104],"for":[68,78],"automatically":[69],"handling":[70],"streams":[72],"interface":[77],"performing":[79,114],"interactive":[80,100],"analysis.":[81],"Based":[82],"on":[83,120],"features":[85],"frauds,":[88],"we":[89],"present":[90],"detection":[92,109,119],"solution":[93],"which":[94],"balances":[95],"computationally":[96],"expensive":[97],"algorithms":[98],"analysis":[101,129],"between":[102],"analysts.":[106],"Specifically,":[107],"our":[108,153],"filters":[111],"an":[115],"initial":[116],"suspicion":[117],"level":[118],"server,":[122],"analysts":[124],"can":[125,167],"combine":[126],"different":[127,159],"statistical":[128],"user":[132],"/":[133],"item":[134],"matrix":[135,145],"co-mapped":[138],"singular":[139],"value":[140],"decomposition":[141],"(SVD)":[142],"diagram,":[143],"re-ordered":[144],"representation,":[146],"temporal":[149],"view.":[150],"We":[151],"demonstrate":[152],"approach":[154],"case":[156],"studies":[157],"scenarios":[161],"show":[163],"be":[168],"effectively":[169],"detected.":[170]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
