Long sequences with a lot of events (LoLo): A visual analytics approach for analyzing long event sequences

Event sequence data consists of discrete events that happen over time. By grouping events based on common entities and ordering them chronologically, they form sequences. Events are registered in different domains, ranging from healthcare to logistics. Collections of these sequences typically represent high-level processes for users to discover, identify, and analyze. This discovery is challenging, given that sequences in real-world scenarios can grow long, have many events, many attribute dimensions of events, and/or various event categories. However, limited research focuses on analyzing long event sequences, the focus of this paper. We present LoLo, an interactive visual analytics method based on the analysis of multi-level structures in long event sequence collections. LoLo introduces a strategy to split the sequence collection into meaningful data-driven stages, where the definition of a stage facilitates interpretation and injection of domain knowledge. The stages have different levels, which represent high-level processes taking into account high-level changes (global staging) combined with local sequence variations (local staging). We demonstrate the effectiveness of LoLo by comparing it to a baseline and present two use cases, one is evaluated with two users and the other by us, on real-world data sets showing that our staging method can capture the semantic content in stages and users appreciate being able to switch between different levels of detail.