In this paper we present a methodology based on combining sequence learning and case-based reasoning. This methodology has been applied in the analysis, mining and recognition of sequential data provided by complex systems with the aim of anticipating failures. Our objective is to extract valuable sequences from log data and integrate them on a case-based reasoning system in order to make predictions based on past experiences. We have used an Apriori---style algorithm (CloSpan) to extract patterns from original data. Afterwards, we have extended our own tool (eXiT*CBR) to deal with sequences in a case-based reasoning environment. The results have shown that our methodology anticipated correctly the failures in most of the cases.
['Marc Compta', 'Beatriz López']
Integration of sequence learning and CBR for complex equipment failure prediction