Trending Pool: Visual Analytics for Trending Event Compositions for Time-Series Categorical Log Data

Although many visualization tools provide us plenty of ways to view the data, users can not easily find the trending events and their explanation from the data. In this work, we address the issue by leveraging the real music streaming log data as an example to better understand a million-scale dataset. Trending event explanation turns out to be challenging when it comes to categorical log data. Therefore, we propose to use a learning-based method with an interface design to uncover the trending event compositions for time-series categorical log data, which can be extend to other datasets, e.g., the hashtags in social media. First, we perform "trending pool" operation to save the memory and time cost. Second, we apply sparse coding to learn important trending candidate combination sets instead of traditional brute-force way or manual investigation for generating combinations. Besides the contributions above, we also observe some interesting user behaviors by exploring detected trending candidate combinations visually through our interface.