  One of the most important assets of any company is being able to easily
access information on itself and on its business. In this line, it has been
observed that this important information is often stored in one of the millions
of spreadsheets created every year, due to simplicity in using and manipulating
such an artifact. Unfortunately, in many cases it is quite difficult to
retrieve the intended information from a spreadsheet: information is often
stored in a huge unstructured matrix, with no care for readability or
comprehensiveness. In an attempt to aid users in the task of extracting
information from a spreadsheet, researchers have been working on models,
languages and tools to query. In this paper we present an empirical study
evaluating such proposals assessing their usage to query spreadsheets. We
investigate the use of the Google Query Function, textual model-driven
querying, and visual model-driven querying. To compare these different querying
approaches we present an empirical study whose results show that the end-users'
productivity increases when using model-driven queries, specially using its
visual representation.
