Abstract:This paper considers predictive maintenance, which is the task of predicting rare and anomalous events (typically, system failures) using event logs data, which are series of timestamped symbolic codes emitted at regular or irregular intervals by a monitored system. Our objective is to find small sets of codes (called itemsets or patterns) that occur shortly before failures. Current prediction methods either produce patterns at a high computational cost or resort to kernel approaches which are often difficult … Show more
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