Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F159

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 77–80 (2020)

Full text

Abstract. FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction was the seventh edition of the international data mining competition organized at Knowledge Pit, in association with the Conference on Computer Science and Information Systems. The main goal was to answer the question of whether it is possible to reliably predict workload-related characteristics of monitored network devices based on historical readings. We describe the scope and explain the motivation for this challenge. We also analyze solutions uploaded by the most successful participants and investigate prediction errors which had the greatest influence on the results. Finally, we describe our baseline solution to the considered problem, which turned out to be the most reliable in the final evaluation.

References

1. A. Chądzyńska-Krasowska and M. Kowalski. Quality of Histograms as Indicator of Approximate Query Quality. In Proc. of FedCSIS 2016, pages 9–15.
2. H. M. Hashemian. State-of-the-Art Predictive Maintenance Techniques. IEEE Trans. Instrum. Meas., 60(1):226–236, 2011.
3. C. Liu. Shallow, Deep, Ensemble Models for Network Device Workload Forecasting. In Proc. of FedCSIS 2020.
4. L. McInnes, J. Healy, N. Saul, and L. Großberger. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw., 3(29):861, 2018.
5. A. Mueen and E. Keogh. Online Discovery and Maintenance of Time Series Motifs. In Proc. of KDD 2010, pages 1089–1098.
6. D. Ruta, L. Cen, and Q. H. Vu. Deep Bi-Directional LSTM Networks for Device Workload Forecasting. In Proc. of FedCSIS 2020.
7. Ł. Sosnowski and T. Penza. Generating Fuzzy Linguistic Summaries for Menstrual Cycles. In Proc. of FedCSIS 2020.
8. G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Trans. Ind. Informatics, 11(3):812–820, 2015.
9. T. Wittkopp, A. Acker, S. Nedelkoski, J. Bogatinovski, and O. Kao. Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction. In Proc. of FedCSIS 2020.
10. M. Zuefle and S. Kounev. A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation. In Proc. of FedCSIS 2020.