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References [1] D. J. Abadi, D. Carney, U. Çetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S. Zdonik, “Aurora: A New Model and Architecture for Data Stream Management,” Internat. J. Very Large Data Bases (VLDB J.), 12:2 (2003), 120-139. [2] S. Acharya, P. B. Gibbons, and V. Poosala, “Congressional Samples for Approximate Answering of Group-By Queries,” Proc. ACM SIGMOD Internat. Conf. on Management of Data (SIGMOD ’00) (Dallas, TX, 2000), pp. 487-498. [3] S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy, “Join Synopses for

data, Biometrics 54(2): 401-415. Ditzler, G., Roveri, M., Alippi, C. and Polikar, R. (2015). Learning in nonstationary environments: A survey, IEEE Computational Intelligence Magazine 10(4): 12-25. Domingos, P. and Hulten, G. (2000). Mining high-speed data streams, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, pp. 71-80. Duda, P., Jaworski, M. and Rutkowski, L. (2017). Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences

References [1] A. Agarwal, O. Chapelle, M. Dudik and J. Langford. A Reliable Effective Terascale Linear Learning System. Journal of Machine Learning Research, 15, 2014, pp. 1111 - 1133. [2] V. Bhambri. Data Mining as a Tool to Predict Churn Behaviour of Customers. International Journal of Management Research, April 2013, pp. 59 - 69. [3] P. Domingos and G. Hulten. Mining high-speed data streams. KDD 2000, pp. 71 - 80. [4] P. Dhandayudam and I. Krishnamurthi. Customer Behavior Analysis Using Rough Set Approach. Journal of Theoretical and Applied Electronic

References Aggarwal, C.C., Han, J., Wang, J. and Yu, P.S. (2003). A framework for clustering evolving data streams, Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany, pp. 81-92. Allan, J., Papka, R. and Lavrenko, V. (1998). On-line new event detection and tracking, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), Melbourne, Australia , pp. 37-45. Amati, G., Amodeo, G. and Gaibisso, C. (2012). Survival analysis for freshness in

References [1] Abdulsalam, H., Martin, P., and Skillicorn, D. S.; Streaming random forests. In 11th International Database Engineering and Applications Symposium (IDEAS 2007), pp. 225–232. [2] Abdulsalam, H., Skillicorn, D. B., and Martin, P.; Classifying evolving data streams using dynamic streaming random forests. In International Conference on Database and Expert Systems Applications (2008), Springer, pp. 643–651. [3] Baena-Garcia, M., del Campo-Avila, J., Fidalgo, R., Bifet, A., Gavalda, R., and Morales-Bueno, R.; Early drift detection method. In Fourth


The recently deployed supercomputer Tryton, located in the Academic Computer Center of Gdansk University of Technology, provides great means for massive parallel processing. Moreover, the status of the Center as one of the main network nodes in the PIONIER network enables the fast and reliable transfer of data produced by miscellaneous devices scattered in the area of the whole country. The typical examples of such data are streams containing radio-telescope and satellite observations. Their analysis, especially with real-time constraints, can be challenging and requires the usage of dedicated software components. We propose a solution for such parallel analysis using the supercomputer, supervised by the KASKADA platform, which with the conjunction with immerse 3D visualization techniques can be used to solve problems such as pulsar detection and chronometric or oil-spill simulation on the sea surface.

data streams, Information Sciences 265: 50-67. Che, D., Safran, M. and Peng, Z. (2013). From big data to big data mining: Challenges, issues, and opportunities, in B. Hong et al. (Eds.), International Conference on Database Systems for Advanced Applications, Lecture Notes in Computer Science, Vol. 7827, Springer, Berlin/Heidelberg, pp. 1-15. Chen, C.L.P. and Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data, Information Sciences 275(10): 314-347. Custers, B., Calders, T., Schermer, B. and Zarsky, T.Z. (Eds

: Methodology and Applications, P. Angelov, D. Filev, N. Kasabov Ed., NY: John Wiley & Sons, 2008, pp. 446-464. [8] C. S. Möller-Levet, F. Klawonn, K.-H. Cho and O. Wolkenhauer, “Fuzzy clustering of short time series and unevenly distributed sampling points,” in Advances in Intelligent Data Analysis V (Lecture Notes in Computers Science), Vol. 2810, Heidelberg: Springer, 2003, pp. 330-340. [9] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, IOS Press, 2010. [10] M. M. Gaber, A. Zaslavsky

. and Gawron, P. (2017). SymmetricTensors.jl, Zenodo , DOI: 10.5281/zenodo.996222. Domino, K., Pawela, Ł. and Gawron, P. (2018b). Efficient computation of higer-order cumulant tensors, SIAM Journal on Scientific Computing 40 (3): A1590–A1610. Gama, J. (2010). Knowledge Discovery from Data Streams , Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Vol. 20103856, Chapman and Hall/CRC, Boca Raton, FL. Geng, M., Liang, H. and Wang, J. (2011). Research on methods of higher-order statistics for phase difference detection and frequency estimation, 4th

References [1] An A., Learning Classification Rules from Data, Computers and Mathematics with Applications, vol. 45, p. 737-748, 2003. [2] Baena-Garcia M., Del Campo-Avila J., Fidalgo R., Bifet A., Early Drift Detection Method, Proceedings of the 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, p. 77-86, Berlin, Germany, 2006. [3] Bakker J., Pechenizkiy M., Food Wholesales Prediction: What is Your Baseline?, Proceedings of the 18th Symposium on Methodologies for Intelligent Systems, ISMIS 2009, Prague, Czech Republic, LNCS, vol. 5722