{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:33:19Z","timestamp":1743085999963,"version":"3.37.3"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3096039","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T19:41:43Z","timestamp":1625859703000},"page":"97603-97620","source":"Crossref","is-referenced-by-count":3,"title":["Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3010-2150","authenticated-orcid":false,"given":"Nusrat","family":"Shaheen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6711-2363","authenticated-orcid":false,"given":"Basit","family":"Raza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7520-6770","authenticated-orcid":false,"given":"Ahmad Raza","family":"Shahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5569-5629","authenticated-orcid":false,"given":"Ahmad Kamran","family":"Malik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2985646"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.03.047"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2977573"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2992869"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","article-title":"Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","volume":"479","author":"ugo","year":"2019","journal-title":"Inf Sci"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2979812"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162668"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TEM.2021.3059664"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2020.2971952"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9320-8"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4294095"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2767044"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS.2009.202"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAE.2010.5452007"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SERA.2010.11"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS.2009.203"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/DSDE.2010.72"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3000139"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC.2008.12"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196908"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342646"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/WAINA.2009.159"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2016.03.012"},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106216","article-title":"Autonomic performance prediction framework for data warehouse queries using lazy learning approach","volume":"91","author":"raza","year":"2020","journal-title":"Appl Soft Comput"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICET.2018.8603615"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref5","first-page":"1","article-title":"Automatic workload characterization using system log analysis","author":"awad","year":"2015","journal-title":"Proc 41st Int IT Capacity Perform Conf"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3154979.3155003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2856127"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2018.04.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1082983.1083082"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1272-0"},{"key":"ref46","first-page":"2677","article-title":"An extension on &#x2018;statistical comparisons of classifiers over multiple data sets&#x2019; for all pairwise comparisons","volume":"9","author":"garc\u00eda","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1389-3"},{"key":"ref45","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.14778\/2536206.2536219"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.2478\/v10006-012-0064-z"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1145\/2938503.2938552","article-title":"Predicting SQL query execution time for large data volume","author":"singhal","year":"2016","journal-title":"Proc Int Database Eng Appl Symp"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2523949"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/TST.2013.6678907","article-title":"Performance prediction for performance-sensitive queries based on algorithmic complexity","volume":"18","author":"chi","year":"2013","journal-title":"Tsinghua Sci Technol"},{"journal-title":"TPC-Homepage V5","year":"2020","key":"ref41"},{"key":"ref23","first-page":"1081","article-title":"Predicting query execution time: Are optimizer cost models really unusable?","author":"wu","year":"2013","journal-title":"Proc IEEE 29th Int Conf Data Eng (ICDE)"},{"journal-title":"KEEL A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems (Regression Classification Clustering Pattern Mining and so on)","year":"2020","key":"ref44"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2467800"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731944"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.05.010"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09478861.pdf?arnumber=9478861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T11:57:30Z","timestamp":1643198250000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9478861\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3096039","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}