Analyzing Time Attributes in Temporal Event Sequences
This work addresses the need for better time attribute analysis in event sequences, such as patient flow in healthcare, but is incremental as it builds on existing visualization techniques.
The authors tackled the problem of analyzing time attributes in temporal event sequences, which existing visualization methods inadequately address, by proposing a visual analytics methodology that identifies trends and outliers in duration and time of occurrence, applied to a real-world hospital dataset to derive four classes of conclusions.
Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present limitations when studying time attributes in event sequences. Time attributes are especially important when studying waiting times or lengths of visit in patient flow analysis. We propose a visual analytics methodology that allows the identification of trends and outliers in respect of duration and time of occurrence in event sequences. The proposed method presents event data using a single Sequential and Time Patterns overview. User-driven alignment by multiple events, sorting by sequence similarity and a novel visual encoding of events allows the comparison of time trends across and within sequences. The proposed visualization allows the derivation of findings that otherwise could not be obtained using traditional visualizations. The proposed methodology has been applied to a real-world dataset provided by Sheffield Teaching Hospitals NHS Foundation Trust, for which four classes of conclusions were derived.