The Critical Role of Data Mining for Analyzing Real World Event Sequences
There are many existing techniques for visualizing temporal data, but the complexity of real world temporal data may undermine certain approaches. Many existing techniques follow consistent and helpful conventions of mapping time to the horizontal axis, aligning multiple timelines along the vertical axis, and assign various event types to categorical visual encodings. A variety of techniques, such as LifeLines [7] and EventFlow [3] have shown great promise in aiding the analysis of temporal patterns. However, as the volume of temporal data increases, along with the data's inherent noise, we argue that visualization and interaction techniques alone may not empower analysts sufficiently. In particular, the diversity of event types, the presence of both relevant and irrelevant event types mixed together, and the lack of strict orderliness in real world data pose many challenges. To this effect, we argue that integrating data mining techniques is critical for making sense out of real world temporal data for many domains. While automated data mining techniques can't be a substitute for user centered exploration, data mining techniques can empower users in critical ways. Visualizing entire event sequences because impossible as the volume of data grows. Simplifying raw event sequences using filtering and aggregation is promising [3] but this may create a reliance of domain expertise. Often, it may be hard to see patterns due to unimportant events distributed through the sequences. In addition, temporal visualization techniques often do not scale to thousands of different event types. However, if we can utilize data mining techniques that effectively unearth statistically significant patterns, simpler and more concise visualizations can be used for communication by focusing on these patterns.