  Big data analysis has become an active area of study with the growth of
machine learning techniques. To properly analyze data, it is important to
maintain high-quality data. Thus, research on data cleaning is also important.
It is difficult to automatically detect and correct inconsistent values for
data requiring expert knowledge or data created by many contributors, such as
integrated data from heterogeneous data sources. An example of such data is
metadata for scientific datasets, which should be confirmed by data managers
while handling the data. To support the efficient cleaning of data by data
managers, we propose a data cleaning architecture in which data managers
interactively browse and correct portions of data through views. In this paper,
we explain our view-based data cleaning architecture and discuss some remaining
issues.
