Event regularity and irregularity in a time unit

In this paper, we study the problem of learning a regular model from a number of sequences, each of which contains events in a time unit. Assuming some regularity in such sequences, we determine what events should be deemed irregular in their contexts. We perform an in-depth analysis of the model we build, and propose two optimization techniques, one of which is also of independent interest in solving a new problem named the Group Counting problem. Our comprehensive experiments on real and hybrid datasets show that the model we build is very effective in characterizing regularities and identifying irregular events. One of our optimizations improves model building speed by more than an order of magnitude, and the other significantly saves space consumption.