  Task abstractions and taxonomic structures for tasks are useful for designers
of interactive data analysis approaches, serving as design targets and
evaluation criteria alike. For individual data types, dataset-specific
taxonomic structures capture unique data characteristics, while being
generalizable across application domains. The creation of dataset-centric but
domain-agnostic taxonomic structures is difficult, especially if best practices
for a focused data type are still missing, observing experts is not feasible,
and means for reflection and generalization are scarce. We discovered this need
for methodological support when working with time-stamped event sequences, a
datatype that has not yet been fully systematically studied in visualization
research. To address this shortcoming, we present a methodology that enables
researchers to abstract tasks and build dataset-centric taxonomic structures in
five phases (data collection, coding, task categorization, task synthesis, and
action-target(criterion) crosscut). We validate the methodology by applying it
to time-stamped event sequences and present a task typology that uses triples
as a novel language of description for tasks: (1) action, (2) data target, and
(3) data criterion. We further evaluate the descriptive power of the typology
with a real-world case on cybersecurity.
