PhD Proposal: Beyond Domain Boundaries: Enhancing The Generalizability of Event Sequence Analytics

Event sequence data is critical across domains like healthcare and industrial operations, yet the field of event sequence visual analytics remains highly fragmented. The current landscape is rich in domain-specific solutions but poor in generalizable principles. This dissertation addresses this fragmentation by establishing methodologies to enhance generalizability across three fundamental dimensions: theoretical, computational, and empirical.
First, we introduce a theoretical, domain-agnostic task framework that provides a shared analytical vocabulary to seamlessly transfer knowledge across disparate application areas. Second, we present a computational causal framework that extends treatment effect estimation to the temporal point process setting, enabling the robust extraction of causal relations under non-stationary conditions. Finally, we provide two empirical contributions to address evaluation bottlenecks. First, we conduct a crowdsourced experiment that identifies visual complexity as a primary factor affecting human comprehension. Second, we introduce ProcVQA, a novel benchmark designed to evaluate the baseline structural comprehension of Vision-Language Models (VLMs), providing the foundation required to utilize AI as a scalable, automated evaluation tool.