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DILAF: A framework for distributed analysis of large-scale system logs for anomaly detection
(Wiley, 2019-02)
System logs constitute a rich source of information for detection and prediction of anomalies. However, they can include a huge volume of data, which is usually unstructured or semistructured. We introduce DILAF, a framework ...
Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
(Elsevier, 2019-05)
Commensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the ...
Incremental analysis of large-scale system logs for anomaly detection
(IEEE, 2019)
Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations ...
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