Data analysts may not always have the data they need to effectively carry o
ut the analysis task and, in certain circumstances, these data can only be
derived or generated using existing information. In this paper, we describe
a problem solving framework which involves data generation as an important
component and present a complex real-world application involving mass spec
tral data analysis where a substantial amount of domain knowledge is requir
ed to infer an essential set of data crucial for achieving the application
task. We show how the framework is applied to achieve the analysis task and
how relevant domain knowledge was acquired, represented and applied to gen
erate the data set. Finally the work is evaluated and its implications are
discussed. (C) 1998 Elsevier Science B.V. Ail rights reserved.