In this paper, we investigate a text sparsification technique based on
the identification of local maxima. In particular, we first show that
looking for an order of the alphabet symbols that minimizes the number
of local maxima in a given string is an NP-hard problem.
Successively, we describe how the local maxima sparsification
technique can be used to filter the access to unstructured texts.
Finally, we experimentally show that this approach can be successfully
used in order to create a space efficient index for searching a DNA
sequence as quickly as a full index.
