  Data completeness is an essential aspect of data quality, and has in turn a
huge impact on the effective management of companies. For example, statistics
are computed and audits are conducted in companies by implicitly placing the
strong assumption that the analysed data are complete. In this work, we are
interested in studying the problem of completeness of data produced by business
processes, to the aim of automatically assessing whether a given database query
can be answered with complete information in a certain state of the process. We
formalize so-called quality-aware processes that create data in the real world
and store it in the company's information system possibly at a later point.
