Abstract
In this paper we describe algorithms for computing the BWT and for building (compressed) indexes in external memory. The innovative feature of our algorithms is that they are lightweight in the sense that, for an input of size n, they use only n bits of disk working space while all previous approaches use Θ(n logn) bits of disk working space. Moreover, our algorithms access disk data only via sequential scans, thus they take full advantage of modern disk features that make sequential disk accesses much faster than random accesses.
We also present a scan-based algorithm for inverting the BWT that uses Θ(n) bits of working space, and a lightweight internal-memory algorithm for computing the BWT which is the fastest in the literature when the available working space is o(n) bits.
Finally, we prove lower bounds on the complexity of computing and inverting the BWT via sequential scans in terms of the classic product: internal-memory space × number of passes over the disk data, showing that our algorithms are within an O(logn) factor of the optimal.
The first author has been partially supported by Yahoo! Research and FIRB Linguistica 2006. The second author has been partially funded by Millennium Institute for Cell Dynamics and Biotechnology (ICDB), Grant ICM P05-001-F, Mideplan, Chile.
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Ferragina, P., Gagie, T., Manzini, G. (2010). Lightweight Data Indexing and Compression in External Memory. In: López-Ortiz, A. (eds) LATIN 2010: Theoretical Informatics. LATIN 2010. Lecture Notes in Computer Science, vol 6034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12200-2_60
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DOI: https://doi.org/10.1007/978-3-642-12200-2_60
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