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In the data deluge context, pattern recognition or labeling in streams isbecoming quite an essential and pressing task as data flows inside alwaysbigger streams. The assessment of such tasks is not so easy when dealing withtemporal data, namely patterns that have a duration (a beginning and an endtime-stamp). This paper details an approach based on an editing distance tofirst align a sequence of labeled temporal segments with a ground truthsequence, and then, by back-tracing an optimal alignment path, to provide aconfusion matrix at the label level. From this confusion matrix, standardevaluation measures can easily be derived as well as other measures such as the"latency" that can be quite important in (early) pattern detectionapplications.

Assessing pattern recognition or labeling in streams of temporal data