  Entity Resolution (ER) is the task of finding entity profiles that correspond
to the same real-world entity. Progressive ER aims to efficiently resolve large
datasets when limited time and/or computational resources are available. In
practice, its goal is to provide the best possible partial solution by
approximating the optimal comparison order of the entity profiles. So far,
Progressive ER has only been examined in the context of structured (relational)
data sources, as the existing methods rely on schema knowledge to save
unnecessary comparisons: they restrict their search space to similar entities
with the help of schema-based blocking keys (i.e., signatures that represent
the entity profiles). As a result, these solutions are not applicable in Big
Data integration applications, which involve large and heterogeneous datasets,
such as relational and RDF databases, JSON files, Web corpus etc. To cover this
gap, we propose a family of schema-agnostic Progressive ER methods, which do
not require schema information, thus applying to heterogeneous data sources of
any schema variety. First, we introduce two naive schema-agnostic methods,
showing that straightforward solutions exhibit a poor performance that does not
scale well to large volumes of data. Then, we propose four different advanced
methods. Through an extensive experimental evaluation over 7 real-world,
established datasets, we show that all the advanced methods outperform to a
significant extent both the na\"ive and the state-of-the-art schema-based ones.
We also investigate the relative performance of the advanced methods, providing
guidelines on the method selection.
