G
Giovanni Manzini
Researcher at University of Eastern Piedmont
Publications - 158
Citations - 6454
Giovanni Manzini is an academic researcher from University of Eastern Piedmont. The author has contributed to research in topics: Data compression & Suffix array. The author has an hindex of 35, co-authored 152 publications receiving 6062 citations. Previous affiliations of Giovanni Manzini include University of Turin & National Research Council.
Papers
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Proceedings ArticleDOI
Opportunistic data structures with applications
Paolo Ferragina,Giovanni Manzini +1 more
TL;DR: A data structure whose space occupancy is a function of the entropy of the underlying data set is devised, which achieves sublinear space and sublinear query time complexity and is shown how to plug into the Glimpse tool.
Journal ArticleDOI
Indexing compressed text
Paolo Ferragina,Giovanni Manzini +1 more
TL;DR: Two compressed data structures for the full-text indexing problem that support efficient substring searches using roughly the space required for storing the text in compressed form are designed and exploits the interplay between two compressors: the Burrows--Wheeler Transform and the LZ78 algorithm.
Journal ArticleDOI
Compressed representations of sequences and full-text indexes
TL;DR: The FM-index is the first that removes the alphabet-size dependance from all query times and the compressed representation of integer sequences with a compression boosting technique to design compressed full-text indexes that scale well with the size of the input alphabet Σ.
Journal ArticleDOI
An analysis of the Burrows—Wheeler transform
TL;DR: In this article, it was shown that the compression ratio of Gzip and Gzip can be bounded in terms of the kth order empirical entropy of the input string for any k ≥ 0.
Proceedings ArticleDOI
An experimental study of an opportunistic index
Paolo Ferragina,Giovanni Manzini +1 more
TL;DR: This index combines the compression algorithm proposed by Burrows and Wheeler with the suffix array data structure and is opportunistic in that it takes advantage of the compressibility of the input data by decreasing the space occupancy at no significant asymptotic slowdown in the query performance.