Temporal data has always captured people's imagination. Large databases of temporal data contain temporal patterns that can lead to the discovery of important cause-and-effect phenomena. Since discovering these patterns is a difficult task, there is a great opportunity to improve support for searching. Temporal analysis of, for example, medical records, web server logs, legal, academic, or criminal records can benefit from more effective search strategies.
This dissertation describes several interactive visualization techniques designed to enhance analysts' experience in performing search, exploration, and summarization of multiple sets of temporal categorical data. These techniques are implemented in the software Lifelines2. Lifelines2 is an interactive visualization system that enables analysts to dynamically change their focus in order to expose temporal ordering of event sequences and study the prevalence of such orderings.
This dissertation makes four main contributions. The first three are technical contributions, and the last is a process model that generalizes user behavior. First, the Align-Rank-Filter framework is presented to help analysts perform visual search and exploration. It enables analysts to center their attention on temporal events that are the focus of their inquiry. Through a controlled experiment, alignment alone is shown to improve user performance speed by up to 60\% in tasks that require understanding of temporal ordering of events. The initial successful exploration on the alignment operator led to its fuller exploitation. Further enhancements to filtering are presented to better incorporate alignment. Second, I designed and implemented the Temporal Pattern Search (TPS) algorithm for filtering to support the common, but difficult-to-specify absence of operator in a temporal pattern. TPS exploits the data structure of the visualization system, and it compares favorably to existing common approaches. Third, I present the temporal summaries technique as an overview to support grouping and comparison features in Lifelines2. They support higher-level tasks such as hypothesis generation. These features take advantage of alignment, and the entirety of the system is evaluated in several long-term case studies with domain experts working on their own problems. Fourth, from these long-term case studies, I generalize a process model that describes analyst behavior in searching and interacting with temporal categorical data. Gleaning from observations in the case studies, collaborators' interviews and commentaries, and logs of Lifelines2 usage, I recommend feature design guidelines for future visualization designers for temporal categorical data.
The enthusiasm of the domain experts who used Lifelines2, the changing strategies for problem-solving, and their initial successes suggest these interactive visualization techniques are a valuable addition to search capabilities.