Reflecting the growing importance of longitudinal and sequential event analysis to many fields, researchers have proposed a variety of visualization methods and interactive visual analysis techniques in recent years. Early work focused on visualizing all permutations, and approach which scales poorly when applied to large-scale datasets with many event types and long sequence lengths. More recently, a variety of proposed techniques use methods such as simplification algorithms, manual user-driven simplification interaction capabilities, pattern mining algorithms, and statistical prioritization to overcome these scalability challenges. However, these methods commonly seek explicit sequential patterns of the form ‘Event A before Event B’ subject to certain time constraints (e.g., within 60 days) or boolean operators (e.g., ‘Event A or Event B’). This paper asserts that this approach is not sufficient for certain high-dimensional real-world event analysis applications. Instead, it argues that new research is needed to develop methods and tools that support soft pattern discovery and analysis.