{"id":"https://openalex.org/W3133870215","doi":"https://doi.org/10.1109/tkde.2021.3062707","title":"Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach","display_name":"Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach","publication_year":2021,"publication_date":"2021-03-01","ids":{"openalex":"https://openalex.org/W3133870215","doi":"https://doi.org/10.1109/tkde.2021.3062707","mag":"3133870215"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2021.3062707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3062707","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-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/A5013889855","display_name":"Xiuwen Yi","orcid":"https://orcid.org/0000-0003-2703-6794"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiuwen Yi","raw_affiliation_strings":["JD Intelligent Cities Research, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2703-6794","affiliations":[{"raw_affiliation_string":"JD Intelligent Cities Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045164115","display_name":"Xiaodu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodu Yang","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-4998-7822","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071469942","display_name":"Yanyong Huang","orcid":"https://orcid.org/0000-0001-9322-2777"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyong Huang","raw_affiliation_strings":["School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026716125","display_name":"Songyu Ke","orcid":"https://orcid.org/0000-0001-7184-8074"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songyu Ke","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100778479","display_name":"Junbo Zhang","orcid":"https://orcid.org/0000-0001-5947-1374"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]},{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbo Zhang","raw_affiliation_strings":["JD Intelligent Cities Research, Beijing, China","Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-5947-1374","affiliations":[{"raw_affiliation_string":"JD Intelligent Cities Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070559820","display_name":"Tianrui Li","orcid":"https://orcid.org/0000-0001-7780-104X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Li","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-7780-104X","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100681023","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0002-5224-4344"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]},{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["JD Intelligent Cities Research, Beijing, China","Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-5224-4344","affiliations":[{"raw_affiliation_string":"JD Intelligent Cities Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5013889855"],"corresponding_institution_ids":["https://openalex.org/I4210103986"],"apc_list":null,"apc_paid":null,"fwci":0.8399,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77896172,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"34","issue":"12","first_page":"5796","last_page":"5808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T11220","display_name":"Water Systems and Optimization","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7848836183547974},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6515694260597229},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5031084418296814},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4865531027317047},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.41546595096588135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37739434838294983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35313016176223755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848836183547974},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6515694260597229},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5031084418296814},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4865531027317047},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.41546595096588135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37739434838294983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35313016176223755},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2021.3062707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3062707","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.6000000238418579,"display_name":"No poverty"}],"awards":[{"id":"https://openalex.org/G3013159334","display_name":null,"funder_award_id":"JBK2101001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3258293943","display_name":null,"funder_award_id":"Z201100006820053","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"},{"id":"https://openalex.org/G7572955247","display_name":null,"funder_award_id":"61773324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7668086135","display_name":null,"funder_award_id":"72061127001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8743324561","display_name":null,"funder_award_id":"62076191","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329795","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822"},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W192546878","https://openalex.org/W1512298495","https://openalex.org/W1673310716","https://openalex.org/W1892382968","https://openalex.org/W1919544272","https://openalex.org/W1978551333","https://openalex.org/W1980564965","https://openalex.org/W1995443851","https://openalex.org/W1996021349","https://openalex.org/W1999432478","https://openalex.org/W2021635209","https://openalex.org/W2023387756","https://openalex.org/W2102828313","https://openalex.org/W2105497548","https://openalex.org/W2112738128","https://openalex.org/W2122646361","https://openalex.org/W2144182447","https://openalex.org/W2159637520","https://openalex.org/W2164223054","https://openalex.org/W2170673311","https://openalex.org/W2188715005","https://openalex.org/W2466038529","https://openalex.org/W2498300967","https://openalex.org/W2515500989","https://openalex.org/W2566832792","https://openalex.org/W2613480438","https://openalex.org/W2785362611","https://openalex.org/W2786088545","https://openalex.org/W2789012589","https://openalex.org/W2795138333","https://openalex.org/W2884934420","https://openalex.org/W2948517885","https://openalex.org/W2951326654","https://openalex.org/W2956046297","https://openalex.org/W3094324010","https://openalex.org/W3098957257","https://openalex.org/W3105931142","https://openalex.org/W6637131181","https://openalex.org/W6683199847","https://openalex.org/W6683941694","https://openalex.org/W6727654133","https://openalex.org/W6748102297"],"related_works":["https://openalex.org/W3194539120","https://openalex.org/W2937631562","https://openalex.org/W4361795583","https://openalex.org/W3136979370","https://openalex.org/W3195168932","https://openalex.org/W1996541855","https://openalex.org/W2979979539","https://openalex.org/W2101819884","https://openalex.org/W2803710604","https://openalex.org/W3127425528"],"abstract_inverted_index":{"The":[0,222],"natural":[1],"gas":[2,20,65,134,247],"tightly":[3],"correlates":[4],"with":[5],"our":[6,228],"everyday":[7],"life.":[8],"However,":[9],"driven":[10],"by":[11,21],"gray":[12],"incomes,":[13],"some":[14],"users":[15,32,154,192],"are":[16,193],"prone":[17],"to":[18,68,93,149,168,177],"stealing":[19],"refitting":[22],"the":[23,29,47,56,74,129,133,137,140,164,182,187,196,206,240],"equipment":[24],"without":[25],"permission.":[26],"Especially":[27],"for":[28,198,246],"boiler":[30,98,152],"room":[31,99,153],"in":[33],"winter,":[34],"this":[35,84],"phenomenon":[36],"appears":[37],"more":[38],"rampant.":[39],"Traditional":[40],"gas-theft":[41,75,95,179,244],"detection":[42,109,114,125,142,166],"methods":[43],"highly":[44],"rely":[45],"on":[46,214,239],"on-site":[48],"inspection,":[49],"where":[50,73],"exists":[51],"ineffective":[52],"and":[53,81,116,136,190,200],"randomness.":[54],"With":[55],"rapidly":[57],"deployed":[58],"IoT":[59],"sensors,":[60],"we":[61,86,160],"can":[62,204],"collect":[63],"real-time":[64,237],"consumption":[66,135],"data":[67,107,174],"analyze":[69],"users\u2019":[70],"behavior":[71],"patterns,":[72],"suspects":[76,96,180,245],"could":[77],"be":[78],"discovered":[79],"early":[80],"accurately.":[82],"In":[83],"paper,":[85],"propose":[87],"a":[88,105,111,145,215,236],"data-driven":[89],"approach,":[90],"named":[91],"SVOC,":[92],"detect":[94,150],"among":[97],"users.":[100,185],"Our":[101],"approach":[102,229],"consists":[103],"of":[104,156,227],"scenario-based":[106],"quality":[108],"algorithm,":[110,115],"deformation-based":[112],"normality":[113,141],"an":[117],"One-Class":[118],"Support":[119],"Vector":[120],"Machine":[121],"(OCSVM)":[122],"based":[123],"anomaly":[124,165],"algorithm.":[126],"Specifically,":[127],"considering":[128],"temporal":[130],"proximity":[131],"between":[132],"outdoor":[138],"temperature,":[139],"algorithm":[143,167],"adopts":[144],"similarity-based":[146],"deformation":[147],"correlation":[148],"normal":[151,189],"out":[155],"abnormal":[157,191],"ones.":[158],"Then,":[159],"employ":[161],"OCSVM":[162,197],"as":[163],"capture":[169],"various":[170,231],"features":[171],"across":[172],"multiple":[173],"sources,":[175],"aiming":[176],"distinguish":[178],"from":[181],"remaining":[183],"irregular":[184],"Here,":[186],"detected":[188],"fed":[194],"into":[195],"training":[199],"prediction,":[201],"respectively,":[202],"which":[203],"overcome":[205],"label":[207],"scarcity":[208],"problem.":[209],"We":[210,233],"conduct":[211],"extensive":[212],"experiments":[213],"real-world":[216],"dataset":[217],"during":[218],"one":[219],"heating":[220],"season.":[221],"results":[223],"demonstrate":[224],"distinct":[225],"advantages":[226],"over":[230],"baselines.":[232],"have":[234],"developed":[235],"system":[238],"cloud,":[241],"providing":[242],"daily":[243],"companies.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}