  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.
