Visualization for event sequence data

Abstract

Event sequence data occurs in many domains, ranging for example from healthcare to factory data and is closely related to process mining data. Event data are discrete activities that occur over time with a event category, timestamp, possible other data attributes and a possible duration. These events are chronologically ordered in time and happen to an entity, also called a case. For example, medication events over time of a patient (case). Multiple of these sequences form a sequence collection, often sharing a similar topic, e.g., patients with the similar diseases.  There are still several challenges for analysing this data and users’ domain-knowledge is needed to get relevant insights out of the data. For example, how to deal with many sequences, long sequences, high dimensional events, or how can visualization help with process mining problems. In my PhD, we try to address several of these challenges related to scalability (long sequences or many event attributes), flexibility (how do we define a sequence), comparison (of sequences and visualization methods), conformance checking, and streaming sequences (human performance).

Bio

I am a PhD candidate from the Netherlands (Eindhoven University of Technology (TU/e)) in the visualization/visual analytics group from Anna Vilanova and am currently in Vienna for two months to collaborate with Silvia Miksch. I am in my fourth year of my PhD focused around the visualization of event sequence data. Stef van den Elzen and Anna Vilanova are my supervisors. I also studied at the TU/e. I originally have a bachelor in Industrial Design. Afterward, I did a double master in Industrial Design and Computer Science. I live in Veldhoven (village attached to Eindhoven) with my boyfriend and our two pet birds. My hobbies include traveling, (beach) volleyball, playing electrical guitar, and meeting friends.