  We present a new semi-external algorithm that builds the Burrows--Wheeler
transform variant of Bauer et al. (a.k.a., BCR BWT) in linear expected time.
Our method uses compression techniques to reduce computational costs when the
input is massive and repetitive. Concretely, we build on induced suffix sorting
(ISS) and resort to run-length and grammar compression to maintain our
intermediate results in compact form. Our compression format not only saves
space but also speeds up the required computations. Our experiments show
important space and computation time savings when the text is repetitive. In
moderate-size collections of real human genome assemblies (14.2 GB - 75.05 GB),
our memory peak is, on average, 1.7x smaller than the peak of the
state-of-the-art BCR BWT construction algorithm (\texttt{ropebwt2}), while
running 5x faster. Our current implementation was also able to compute the BCR
BWT of 400 real human genome assemblies (1.2 TB) in 41.21 hours using 118.83 GB
of working memory (around 10\% of the input size). Interestingly, the results
we report in the 1.2 TB file are dominated by the difficulties of scanning huge
files under memory constraints (specifically, I/O operations). This fact
indicates we can perform much better with a more careful implementation of our
method, thus scaling to even bigger sizes efficiently.
