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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 ...
Utilizing clone detection for domain analysis of simulation systems
(IEEE, 2012)
This paper presents a case study on utilizing a clone detection technique for deriving commonalities among four different industrial simulation software systems. We have examined cloning both within each system and across ...
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 ...
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)
Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ...
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