  Data lakes have emerged as an alternative to data warehouses for the storage,
exploration and analysis of big data. In a data lake, data are stored in a raw
state and bear no explicit schema. Thence, an efficient metadata system is
essential to avoid the data lake turning to a so-called data swamp. Existing
works about managing data lake metadata mostly focus on structured and
semi-structured data, with little research on unstructured data. Thus, we
propose in this paper a methodological approach to build and manage a metadata
system that is specific to textual documents in data lakes. First, we make an
inventory of usual and meaningful metadata to extract. Then, we apply some
specific techniques from the text mining and information retrieval domains to
extract, store and reuse these metadata within the COREL research project, in
order to validate our proposals.

Databases