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We summarize IEEE Big Data Cup: Predicting Escalations in Customer Support – a data mining competition organized jointly by companies Information Builders and QED Software at the KnowledgePit platform, in the frame of the 2020 IEEE International Conference on Big Data. We discuss the motivation for organizing this event and highlight the factors that make it such a challenging topic. We describe the data provided to participants and formulate the competition task. We also provide an overview of competition results with a detailed analysis of a few selected solutions. Finally, we present a novel functionality of the KnowledgePit platform – an analytic module that allows organizers to investigate selected solutions using a convenient GUI and provides in-depth insights about their quality.

“IEEE BigData 2019 Cup: Suspicious Network Event Recognition” was a data mining competition organized jointly by companies Security On-Demand and QED Software at the KnowledgePit online platform, in association with the IEEE BigData 2019 conference. The scope of this challenge referred to the notions of cybersecurity analytics and network alert evaluation. In this paper, we summarize the results of our competition. We explain how data sets had been prepared before it was possible to make them available to competition participants. We describe the baseline scoring models that we designed as a reference for participants, and we demonstrate how critical for their performance was the aspect of appropriate feature engineering. We also discuss the results of experiments conducted to verify the (un)suitability of deep recurrent neural networks in this particular case. In some sense, we show that there are no “perfect” machine learning approaches that could be applied equally successfully to every data science undertaking.