Goodbye CINECA! Goodbye Li!

Goodbye CINECA! Goodbye Li! Last week concluded my (almost) two month stay at CINECA in Bologna. I worked on an interesting project and got to experience the life in Bologna. Project The first big part of my project was data

Preprocessing pipeline and train/test set separation

In last blog post I talked about over/under sampling as a method to address unbalanced datasets. As a data transformation method it is important that we are cautious when evaluating performance of classifiers when we are performing over/under sampling. In

Learning on unbalanced classes

The goal of my project is to construct a classifier (learner) that will be able to recognize (and possibly predict) abnormal behavior of HPC system. Naturally abnormal behavior and faults represent relatively small part of the overall data collected from

Swimming in the data lake

Data lake is a storage of large quantities of data that has little to no structure. It combines data from different sources that were originally not intended to be a part of a bigger monitoring infrastructure. In the case of

Martin Molan

My name is Martin Molan. I come from Slovenia and I have just finished the first year of master’s program at IPS at Jozef Stefan Institute in Ljubljana. Before that I obtained a BA in mathematics from University of Ljubljana.

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Anomaly detection of system failures on HPC machines using Machine Learning Techniques

Project reference: 1906 Anomalies detection is one of the timeliest problems in managing HPC facilities. Clearly, this problem involves many technological issues, including big data analysis, machine learning, virtual machines manipulations and authentication protocols. Our research group already prepared a

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