  The use of approximation is fundamental in computational science. Almost all
computational methods adopt approximations in some form in order to obtain a
favourable cost/accuracy trade-off and there are usually many approximations
that could be used. As a result, when a researcher wishes to measure a property
of a system with a computational technique, they are faced with an array of
options. Current computational workflow frameworks focus on helping researchers
automate a sequence of steps on a particular platform. The aim is often to
obtain a computational measurement of a property. However these frameworks are
unaware that there may be a large number of ways to do so. As such, they cannot
support researchers in making these choices during development or at
execution-time.
  We argue that computational workflow frameworks should be designed to be
\textit{approximation-aware} - that is, support the fact that a given workflow
description represents a task that \textit{could} be performed in different
ways. This is key to unlocking the potential of computational workflows to
accelerate discovery tasks, particularly those involving searches of large
entity spaces. It will enable efficiently obtaining measurements of entity
properties, given a set of constraints, by directly leveraging the space of
choices available. In this paper we describe the basic functions that an
approximation-aware workflow framework should provide, how those functions can
be realized in practice, and illustrate some of the powerful capabilities it
would enable, including approximate memoization, surrogate model support, and
automated workflow composition.
