The progressive visual analytics (PVA) paradigm has been proposed to describe visual analytics systems whose main goal is to reach a thorough coupling between the analyst and her system by getting rid of waiting periods classically encountered during data process- ing. PVA systems use algorithms that both provide intermediate results throughout their execution, and are steerable by the analyst to change the strategy used to perform the remaining computation. Our focus is on progressive sequential pattern mining, as in the seminal work of Stolper et al. [30]. Here we go further mainly by considering the temporal nature of patterns related to their occur- rences. We propose a pattern-oriented data model, a pattern analysis task model, and guidelines for designing progressive pattern min- ing algorithms. We introduce PPMT, a tool to support an analyst progressively explore activity traces, based on a modification of the GSP algorithm. We evaluate our proposal on the technical perfor- mances of our progressive algorithm, and on the effect of steering on analysts’ performances.