{"id":"https://openalex.org/W4411624773","doi":"https://doi.org/10.1145/3703323.3703716","title":"Real-time Anomaly Prediction at Scale using Anomaly Detection Augmented with Regression","display_name":"Real-time Anomaly Prediction at Scale using Anomaly Detection Augmented with Regression","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4411624773","doi":"https://doi.org/10.1145/3703323.3703716"},"language":"en","primary_location":{"id":"doi:10.1145/3703323.3703716","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703716","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3703323.3703716","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023588595","display_name":"Kunal Banerjee","orcid":"https://orcid.org/0000-0002-0605-630X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kunal Banerjee","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035399409","display_name":"Binay Gupta","orcid":"https://orcid.org/0000-0001-9304-9329"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Binay Gupta","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010271848","display_name":"Meet Maheshwari","orcid":"https://orcid.org/0000-0003-4538-3845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meet Maheshwari","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006129471","display_name":"Lalitdutt Parsai","orcid":"https://orcid.org/0000-0002-5327-203X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lalitdutt Parsai","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114978358","display_name":"Geet Vudata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geet Vudata","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042303220","display_name":"Soumik Dasgupta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soumik Dasgupta","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075247915","display_name":"Anirban Chatterjee","orcid":"https://orcid.org/0000-0003-2513-6433"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anirban Chatterjee","raw_affiliation_strings":["Walmart Global Tech, Bengaluru, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Bengaluru, Karnataka, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5023588595"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27890262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"183","last_page":"191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9922999739646912,"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/anomaly","display_name":"Anomaly (physics)","score":0.7421289086341858},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7137948870658875},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6408138871192932},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.541043221950531},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4903888702392578},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.42381080985069275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37951740622520447},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35331207513809204},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2390621304512024},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2367534637451172},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16085049510002136},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09278744459152222},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07915347814559937}],"concepts":[{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.7421289086341858},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7137948870658875},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6408138871192932},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.541043221950531},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4903888702392578},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.42381080985069275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37951740622520447},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35331207513809204},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2390621304512024},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2367534637451172},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16085049510002136},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09278744459152222},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07915347814559937},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3703323.3703716","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703716","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3703323.3703716","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703716","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W177404858","https://openalex.org/W1990154365","https://openalex.org/W2034084112","https://openalex.org/W2064675550","https://openalex.org/W2122646361","https://openalex.org/W2135046866","https://openalex.org/W2153635508","https://openalex.org/W2172697690","https://openalex.org/W2295598076","https://openalex.org/W2515822248","https://openalex.org/W2535642622","https://openalex.org/W2909301190","https://openalex.org/W3003089943","https://openalex.org/W3040266635","https://openalex.org/W3106255857","https://openalex.org/W3109037541","https://openalex.org/W3135550350","https://openalex.org/W3192648431","https://openalex.org/W4234173777","https://openalex.org/W6927549558"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Anomalies":[0],"are":[1],"rare":[2],"untoward":[3],"events":[4],"which":[5],"lead":[6],"to":[7,21,106,216,243,250],"system":[8],"failures,":[9],"business":[10],"interruptions":[11],"and":[12,158,182],"bad":[13],"customer":[14],"experiences.":[15],"While":[16],"anomaly":[17,39,218],"detection":[18],"(AD)":[19],"tries":[20],"identify":[22],"these":[23,31,52],"anomalies":[24,50,61,123,233],"as":[25,27,80,220],"quickly":[26],"possible":[28],"so":[29,55,132],"that":[30,56,113,184],"incidents":[32,245],"can":[33,62,150],"be":[34,63,90,151,214],"resolved":[35],"at":[36,227],"the":[37,49,57,87,99,114,122,170,185,189,194,197,200],"earliest,":[38],"prediction":[40],"(AP)":[41],"has":[42,127,138,240],"a":[43,70,76,162],"more":[44,258],"ambitious":[45],"goal":[46],"of":[47,60,86,101,177,193,196],"foretelling":[48],"before":[51],"even":[53],"occur":[54],"damaging":[58],"effects":[59],"avoided":[64],"altogether.":[65],"In":[66,141],"short,":[67],"AD":[68,156,178,190,211],"is":[69,75,111],"reactive":[71],"technique,":[72,84],"whereas":[73],"AP":[74,103,115,148,186],"proactive":[77,83],"technique.":[78],"However,":[79],"with":[81,133,161,174,180],"any":[82,209],"some":[85],"predictions":[88,96,136,219],"may":[89,97,213],"wrong":[91],"\u2013":[92,237],"too":[93],"many":[94],"false":[95,135],"hinder":[98],"adoption":[100],"an":[102,147,154,228],"solution":[104,116,149,157,187,191,212,239],"due":[105],"alert":[107,255],"fatigue.":[108],"Therefore,":[109],"it":[110,160],"important":[112],"not":[117],"only":[118],"identifies":[119],"almost":[120],"all":[121],"in":[124,234,246],"advance":[125,247],"(i.e.,":[126,137],"high":[128,139],"recall)":[129],"but":[130],"does":[131],"minimal":[134],"precision).":[140],"our":[142,238],"work,":[143],"we":[144,223],"showcase":[145],"how":[146,208],"built":[152],"using":[153],"existing":[155,210],"augmenting":[159],"regressor.":[163,201],"We":[164],"carried":[165],"out":[166],"extensive":[167],"experiments":[168],"on":[169,207],"SKAB":[171],"public":[172],"dataset":[173],"multiple":[175],"combinations":[176],"classifiers":[179],"regressors,":[181],"found":[183],"improves":[188],"irrespective":[192],"choice":[195],"classifier":[198],"or":[199],"Thus,":[202],"this":[203,225],"work":[204],"sheds":[205],"light":[206],"extended":[215],"perform":[217],"well.":[221],"Furthermore,":[222],"apply":[224],"technique":[226],"industry-scale":[229],"for":[230],"predicting":[231],"real-time":[232],"Kafka":[235],"channels":[236],"been":[241],"able":[242],"predict":[244],"by":[248,257],"up":[249],"55":[251],"minutes":[252],"while":[253],"reducing":[254],"fatigue":[256],"than":[259],"62%.":[260]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
