A. Bifet and R. Gavalda, Learning from time-changing data with adaptive windowing, Proceedings of the 2007 SIAM Conference, pp.443-448, 2007.

A. Bifet, G. De-francisci, J. Morales, G. Read, B. Holmes et al., Efficient online evaluation of big data stream classifiers, Proceedings of the 21th ACM SIGKDD Conference, pp.59-68, 2015.

D. Brzezinski and J. Stefanowski, Prequential auc: properties of the area under the roc curve for data streams with concept drift, Knowledge and Information Systems, vol.52, issue.2, pp.531-562, 2017.

P. Domingos and G. Hulten, Catching up with the data: Research issues in mining data streams, Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), 2001.

R. Fontugne, P. Borgnat, P. Abry, and K. Fukuda, Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking, Proceedings of the 6th ACM CoNEXT Conference, 2010.
URL : https://hal.archives-ouvertes.fr/ensl-00552071

J. Gama, R. Sebastião, and P. P. Rodrigues, Issues in evaluation of stream learning algorithms, Proceedings of the 15th ACM SIGKDD Conference, pp.329-338, 2009.

J. Gama, R. Sebastião, and P. P. Rodrigues, On evaluating stream learning algorithms, Machine learning, vol.90, issue.3, pp.317-346, 2013.

R. Hoens, R. Polikar, and N. Chawla, Learning from streaming data with concept drift and imbalance: an overview, Progress in Artificial Intelligence, vol.1, issue.1, pp.89-101, 2012.

G. Hulten, P. Domingos, and L. Spencer, Mining massive data streams, 2005.

M. Stonebraker, C. U?guru?gur, and S. Zdonik, The 8 requirements of real-time stream processing, ACM Sigmod Record, vol.34, issue.4, pp.42-47, 2005.