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Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given.<\/jats:p>","DOI":"10.3233\/jifs-211100","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T14:40:04Z","timestamp":1628606404000},"page":"3667-3695","source":"Crossref","is-referenced-by-count":10,"title":["An overview of complex data stream ensemble classification"],"prefix":"10.1177","volume":"41","author":[{"given":"Xilong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}]},{"given":"Meng","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}]},{"given":"Hongxin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}]},{"given":"Muhang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}]},{"given":"Zhiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JIFS-211100_ref1","first-page":"24","article-title":"Review of concept drift data streams mining techniques","volume":"3","author":"Ding","year":"2016","journal-title":"Computer Science"},{"key":"10.3233\/JIFS-211100_ref2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ins.2013.12.011","article-title":"Combining block-based and online methods in learning ensembles from concept drifting data streams","volume":"265","author":"Brzezinski","year":"2014","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-211100_ref3","doi-asserted-by":"crossref","unstructured":"Bifet A. , Holmes G. , Pfahringer B. , Leveraging Bagging for evolving data streams, Proc of the 2010 European conference on Machine learning and knowledge discovery in databases, Berlin, Germany: Springer (2010) 135\u2013150.","DOI":"10.1007\/978-3-642-15880-3_15"},{"key":"10.3233\/JIFS-211100_ref4","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1145\/502512.502568","article-title":"A streaming ensemble algorithm (SEA) for large-scale classification","volume":"6","author":"Street","year":"2001","journal-title":"Proc of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"issue":"36","key":"10.3233\/JIFS-211100_ref5","first-page":"2755","article-title":"Maloof Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts,\u201d","volume":"8","author":"Kolter","year":"2007","journal-title":"Mach Learn"},{"key":"10.3233\/JIFS-211100_ref6","first-page":"107","article-title":"SMOTEBoost: Improving prediction of the Minority Class in Boosting, Proc of Knowledge Discovery in Databases: PKDD 2003","author":"Chawla","year":"2003","journal-title":"Berlin, Germany: Springer"},{"key":"10.3233\/JIFS-211100_ref7","doi-asserted-by":"crossref","first-page":"65103","DOI":"10.1109\/ACCESS.2019.2914725","article-title":"Resample-based ensemble framework for drifting imbalanced data streams","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211100_ref8","doi-asserted-by":"crossref","unstructured":"Kourtellis N. , Morales G.D.F. , Bifet A. , et al., VHT: Vertical hoeffding tree, Proc of 2016 IEEE International Conference on Big Data (Big Data), Washington, USA: IEEE, (2016), 915\u2013922.","DOI":"10.1109\/BigData.2016.7840687"},{"key":"10.3233\/JIFS-211100_ref9","doi-asserted-by":"crossref","unstructured":"Haque A. , Parker B. , Khan L. , Thuraisingham B. , Evolving big data stream classification with MapReduce, Proc of the 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). Anchorage, USA: IEEE, (2014), 570\u2013577.","DOI":"10.1109\/CLOUD.2014.82"},{"key":"10.3233\/JIFS-211100_ref10","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/978-3-319-17551-5_4","article-title":"A survey on supervised classification on data streams","volume":"205","author":"Lemaire","year":"2015","journal-title":"Business Intelligence"},{"issue":"2","key":"10.3233\/JIFS-211100_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3054925","article-title":"A survey on ensemble learning for data stream classification","volume":"50","author":"Gomes","year":"2017","journal-title":"ACM Computing Surveys"},{"key":"10.3233\/JIFS-211100_ref12","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble learning for data stream analysis: a survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Information Fusion"},{"issue":"5","key":"10.3233\/JIFS-211100_ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/4218973","article-title":"Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift","volume":"12","author":"Sun","year":"2016","journal-title":"International Journal of Distributed Sensor Networks"},{"key":"10.3233\/JIFS-211100_ref14","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/ACCESS.2018.2886026","article-title":"An overview on concept drifts learning","volume":"7","author":"Iwashita","year":"2019","journal-title":"IEEE Access"},{"issue":"01","key":"10.3233\/JIFS-211100_ref15","first-page":"15","article-title":"Survey of ensemble classification algorithms for data streams with concept drift","volume":"46","author":"Du","year":"2020","journal-title":"Computer Engineering"},{"key":"10.3233\/JIFS-211100_ref16","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.inffus.2020.09.004","article-title":"Preprocessed dynamicclassifie ensemble selection for highly imbalanced drifted datastreams","volume":"66","author":"Zyblewski","year":"2021","journal-title":"Information Fusion"},{"issue":"5","key":"10.3233\/JIFS-211100_ref17","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.3390\/info10050158","article-title":"Efficient ensemble classification for multi-label data streams with concept drift","volume":"10","author":"Sun","year":"2019","journal-title":"Information"},{"key":"10.3233\/JIFS-211100_ref18","doi-asserted-by":"crossref","first-page":"72713","DOI":"10.1109\/ACCESS.2020.2988120","article-title":"Sampling for Big Data Profiling: A Survey","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211100_ref19","doi-asserted-by":"crossref","unstructured":"Biggio B. , Corona I. , Nelson B. , et al., Security evaluation of support vector machines in adversarial environments, Support Vector Machines Applications. 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Waikoloa:","volume":"3","author":"Oza","year":"2005","journal-title":"IEEE"},{"key":"10.3233\/JIFS-211100_ref22","doi-asserted-by":"crossref","unstructured":"Wang H. , Yu P.S. , Han J. , Mining Concept-Drifting Data Streams, Data Mining & Knowledge Discovery Handbook, (2003), 789\u2013802.","DOI":"10.1007\/978-0-387-09823-4_40"},{"issue":"10","key":"10.3233\/JIFS-211100_ref23","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","article-title":"Incremental learning of concept drift in nonstationary environments","volume":"22","author":"Elwell","year":"2011","journal-title":"IEEE Transactions on Neural Networks"},{"key":"10.3233\/JIFS-211100_ref24","doi-asserted-by":"crossref","unstructured":"Domingos P. , Hulten G. , Mining high-speed data streams, ACM KDD Conference, (2000), 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515\u2013526.","DOI":"10.1007\/s12065-019-00252-3"},{"key":"10.3233\/JIFS-211100_ref28","doi-asserted-by":"crossref","unstructured":"Tsang I. , Kocsor A. , Kwok J. , Simpler core vector machines with enclosing balls, Proc of the Twenty-Fourth International Conference, Oregon: ACM, 227 (2007), 911\u2013918.","DOI":"10.1145\/1273496.1273611"},{"key":"10.3233\/JIFS-211100_ref29","unstructured":"Rai P. , Daum\u00e9 III H. and VenkatasubramanianS., Streamed Learning: One-Pass SVMs, Proc of the 21st International Joint Conference on Artificial Intelligence (2009), 1211\u20131216."},{"key":"10.3233\/JIFS-211100_ref30","first-page":"108","article-title":"An Adaptive Nearest Neighbor ClassificationAlgorithm for Data Streams","volume":"3721","author":"Law","year":"2005","journal-title":"Berlin, Heidelberg: Springer BerlinHeidelberg"},{"key":"10.3233\/JIFS-211100_ref31","first-page":"23","article-title":"Roseberry, Multi-label kNN classifier with self adjusting memory for drifting data streams","volume":"94","author":"Martha","year":"2018","journal-title":"Proc of Machine Learning Research"},{"key":"10.3233\/JIFS-211100_ref32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2012.10.006","article-title":"Evolving granular neural networks from fuzzy data streams","volume":"38","author":"Leite","year":"2013","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-211100_ref33","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/S0031-3203(02)00169-3","article-title":"Double-bagging: combining classifiers by bootstrap aggregation","volume":"36","author":"Torsten Hothorn","year":"2003","journal-title":"Pattern Recognition"},{"issue":"3","key":"10.3233\/JIFS-211100_ref34","doi-asserted-by":"crossref","first-page":"443","DOI":"10.3745\/JIPS.02.0004","article-title":"Extreme learning machine ensemble using bagging for facial expression recognition","volume":"10","author":"Ghimire","year":"2014","journal-title":"Journal of Information 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Stacking Algorithm: A Novel Recognition Method of Space Target ISAR Images Under the Condition of Small Sample Set","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211100_ref42","unstructured":"Cortes C. , Mohri M. , Syed U. , Deep Boosting, Proc of the 31st International Conference on Machine Learning, 32 (2014), 1179\u20131187."},{"issue":"2","key":"10.3233\/JIFS-211100_ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2089094.2089106","article-title":"Ensembles of Restricted Hoeffding Trees","volume":"3","author":"Bifet","year":"2012","journal-title":"Trans. Intell. Syst. Technol."},{"issue":"10","key":"10.3233\/JIFS-211100_ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jclinepi.2009.06.006","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration","volume":"62","author":"Liberati","year":"2009","journal-title":"J. Clin. Epidemiol."},{"issue":"4","key":"10.3233\/JIFS-211100_ref45","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/5326.983933","article-title":"Learn++: an incremental learning algorithm for supervised neural networks","volume":"31","author":"Polikar","year":"2001","journal-title":"IEEE Transactions on Systems, Man and Cybernetics"},{"issue":"2","key":"10.3233\/JIFS-211100_ref46","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11047-007-9063-7","article-title":"Negative correlation in incremental learning","volume":"8","author":"Minku","year":"2009","journal-title":"Natural Computing"},{"key":"10.3233\/JIFS-211100_ref47","doi-asserted-by":"crossref","unstructured":"Zhao Q.L. , Jiang Y.H. , Xu M. , Incremental Learning by Heterogeneous Bagging Ensemble, Proc of Advanced Data Mining & Applications-international Conference, Berlin: Springer, 6441 (2010), 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, Wei F. , Yu P.S. , et al., Mining concept-drifting data streams using ensemble classifiers, Proc of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York: Association for Computing Machinery, (2003), 226\u2013235.","DOI":"10.1145\/956750.956778"},{"key":"10.3233\/JIFS-211100_ref52","doi-asserted-by":"crossref","unstructured":"Deckert M. , Batch Weighted Ensemble for Mining Data Streams with Concept Drift, Berlin: Springer, (2011), 290\u2013299.","DOI":"10.1007\/978-3-642-21916-0_32"},{"issue":"1","key":"10.3233\/JIFS-211100_ref53","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","article-title":"Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm","volume":"25","author":"Brzezinski","year":"2014","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.3233\/JIFS-211100_ref54","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.inffus.2018.01.003","article-title":"An iterative boosting-based ensemble for streaming data classification","volume":"45","author":"Junior","year":"2019","journal-title":"Information Fusion"},{"issue":"1","key":"10.3233\/JIFS-211100_ref55","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4018\/IJGHPC.2019010103","article-title":"Deterministic concept drift detection in ensemble classifier based data stream classification process","volume":"11","author":"Abdualrhman","year":"2019","journal-title":"International Journal of Grid and High Performance Computing"},{"issue":"4","key":"10.3233\/JIFS-211100_ref56","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TKDE.2011.58","article-title":"DDD: A New Ensemble Approach for Dealing withConcept Drift","volume":"24","author":"Minku","year":"2012","journal-title":"IEEE Transactions on Knowledge and 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weighted majority","volume":"9","author":"Sidhu","year":"2018","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.3233\/JIFS-211100_ref60","first-page":"546","article-title":"SMOTE for Predicting Software Build Outcomes, Proc of 26th International Conference on Software Engineering and Knowledge Engineering","author":"Finlay","year":"2014","journal-title":"Vancouver: Knowledge Systems Institute Graduate School"},{"issue":"5","key":"10.3233\/JIFS-211100_ref61","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","article-title":"Resampling-based ensemble methods for online class imbalance learning","volume":"27","author":"Wang","year":"2015","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-211100_ref62","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.comcom.2020.01.061","article-title":"Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model","volume":"153","author":"Ancy","year":"2020","journal-title":"Computer Communications"},{"key":"10.3233\/JIFS-211100_ref63","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.inffus.2020.09.004","article-title":"Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams","volume":"66","author":"Zyblewski","year":"2021","journal-title":"Information Fusion"},{"key":"10.3233\/JIFS-211100_ref64","doi-asserted-by":"crossref","first-page":"65103","DOI":"10.1109\/ACCESS.2019.2914725","article-title":"Resample-based ensemble framework for drifting imbalanced data streams","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211100_ref65","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","article-title":"Cost-sensitive boosting for classification of imbalanced data","volume":"40","author":"Sun","year":"2007","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-211100_ref66","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ins.2019.02.062","article-title":"Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification","volume":"487","author":"Tao","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-211100_ref67","doi-asserted-by":"crossref","unstructured":"Wong M.L. , Seng K. , Wong P.K. , Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain, Expert Systems with Applications 141 (2020).","DOI":"10.1016\/j.eswa.2019.112918"},{"key":"10.3233\/JIFS-211100_ref68","first-page":"498","article-title":"Cost-sensitive learning for imbalanced data streams, Proc of the 35th Annual ACM Symposium on Applied Computing, New York:","volume":"7","author":"Loezer","year":"2020","journal-title":"ACM"},{"issue":"3","key":"10.3233\/JIFS-211100_ref69","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","article-title":"Classifier chains for multi-label classification","volume":"85","author":"Read","year":"2011","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-211100_ref70","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/978-3-319-57529-2_43","article-title":"Weighted ensemble classification of multi-label data streams","volume":"10235","author":"Wang","year":"2017","journal-title":"Advances in Knowledge Discovery and Data Mining"},{"key":"10.3233\/JIFS-211100_ref71","first-page":"302","article-title":"An Online Variational Inference and Ensemble Based Multi-label Classifier for Data Streams, The Eleventh International Conference on Advanced Computational Intelligence, 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classification","volume":"33","author":"Pan","year":"2012","journal-title":"Knowledge Information System"},{"key":"10.3233\/JIFS-211100_ref82","first-page":"1","article-title":"WEC: Weighted Ensemble of Text Classifiers, 2020 IEEE Congress on Evolutionary Computation, Glasgow:","author":"Upadhyay","year":"2020","journal-title":"IEEE"},{"key":"10.3233\/JIFS-211100_ref83","first-page":"12","article-title":"Binary classification of Lupus scientific articles applying deep ensemble model on text data, Proc of 7th International Conference on Digital Information Processing and Communications, Trabzon:","author":"Samami","year":"2019","journal-title":"IEEE"},{"key":"10.3233\/JIFS-211100_ref84","first-page":"289","article-title":"On Supervised Change Detection in Graph Streams, Proc of the 2020 International Conference on Data Mining, Cincinnati:","author":"Aggarwal","year":"2020","journal-title":"SIAM"},{"key":"10.3233\/JIFS-211100_ref85","first-page":"652","article-title":"\u201cOn Classification of Graph Streams, Proc of the Eleventh International Conference on Data Mining Society for Industrial and Applied Mathematics, Mesa:","author":"Aggarwal","year":"2011","journal-title":"SIAM"},{"issue":"5","key":"10.3233\/JIFS-211100_ref86","first-page":"940","article-title":"Graph ensemble boosting for imbalancednoisy graph stream classification","volume":"45","author":"Pan","year":"2015","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"10.3233\/JIFS-211100_ref87","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1109\/TIM.2019.2930157","article-title":"Manifold-preserving sparse graph-based ensemble FDA for industrial label-noise fault classification","volume":"69","author":"Liu","year":"2020","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"2","key":"10.3233\/JIFS-211100_ref88","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10994-014-5465-9","article-title":"Multilabel classification through random graph ensembles","volume":"99","author":"Su","year":"2015","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-211100_ref89","doi-asserted-by":"crossref","first-page":"16568","DOI":"10.1109\/ACCESS.2017.2738069","article-title":"An ensemble random forest algorithm for insurance big data analysis","volume":"5","author":"Lin","year":"2017","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211100_ref90","unstructured":"Mwangi P.I. , Nderu L. , Mwigereri D. , A Stacked Ensemble Model based on RUSBoost and a Cost-Sensitive Convolutional Neural Network for Class Imbalance in Big Data Analytics, Proc of the 2020 African Conference on Software Engineering, Nairobi: CEUR-WS, 2689 (2020)."},{"key":"10.3233\/JIFS-211100_ref91","first-page":"223","article-title":"Low-latency multi-threaded ensemble learning for dynamic big data streams, Proc of International Conference on Big Data, Boston:","author":"Marron","year":"2017","journal-title":"IEEE"},{"key":"10.3233\/JIFS-211100_ref92","doi-asserted-by":"crossref","unstructured":"Denham B. , Pears R. , Naeem M.A. , HDSM: A distributed data mining approach to classifying vertically distributed data streams, Knowledge-Based Systems 189 (2020).","DOI":"10.1016\/j.knosys.2019.105114"},{"key":"10.3233\/JIFS-211100_ref93","doi-asserted-by":"crossref","unstructured":"Gama J. , Medas P. , Rocha R. , Forest Trees for On-line Data, Proc of the 2004 Symposium on Applied Computing, Nicosia: ACM 632\u2013636 (2004).","DOI":"10.1145\/967900.968033"},{"key":"10.3233\/JIFS-211100_ref94","doi-asserted-by":"crossref","unstructured":"Bifet A. , Efficient Online Evaluation of Big Data Stream Classifiers,\u201d Proc of the 21th International Conference on Knowledge Discovery and Data Mining, Sydney: ACM, (2015), 59\u201368.","DOI":"10.1145\/2783258.2783372"},{"issue":"9","key":"10.3233\/JIFS-211100_ref95","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","article-title":"Adaptive random forests for evolving data stream classification","volume":"106","author":"Gomes","year":"2017","journal-title":"Machine Learning"},{"issue":"5","key":"10.3233\/JIFS-211100_ref96","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s10618-019-00654-y","article-title":"Delayed labelling evaluation for data streams","volume":"34","author":"Grzenda","year":"2020","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.3233\/JIFS-211100_ref97","doi-asserted-by":"crossref","unstructured":"Bifet A. , Holmes G. , Pfahringer B. , et al., Fast Perceptron Decision Tree Learning from Evolving Data Streams, Berlin: Springer, (2010), 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