Abstract
Exploring event sequences in big data is challenging. Though many mining algorithms have been developed to derive the most frequently occurring and the most meaningful sequential patterns, it is yet difficult to make sense of the results. To tackle the problem, we introduce a visual analytics ap- proach, Peekquence. In this paper, we describe the design of Peekquence, which aims to increase the interpretability of machine learning-based sequence mining algorithms.
