  Exact string matching has been a fundamental problem in computer science for
decades because of many practical applications. Some are related to common
procedures, such as searching in files and text editors, or, more recently, to
more advanced problems such as pattern detection in Artificial Intelligence and
Bioinformatics. Tens of algorithms and methodologies have been developed for
pattern matching and several programming languages, packages, applications and
online systems exist that can perform exact string matching in biological
sequences. These techniques, however, are limited to searching for specific and
predefined strings in a sequence. In this paper a novel methodology (called
Ex2SM) is presented, which is a pipeline of execution of advanced data
structures and algorithms, explicitly designed for text mining, that can detect
every possible repeated string in multivariate biological sequences. In
contrast to known algorithms in literature, the methodology presented here is
string agnostic, i.e., it does not require an input string to search for it,
rather it can detect every string that exists at least twice, regardless of its
attributes such as length, frequency, alphabet, overlapping etc. The complexity
of the problem solved and the potential of the proposed methodology is
demonstrated with the experimental analysis performed on the entire human
genome. More specifically, all repeated strings with a length of up to 50
characters have been detected, an achievement which is practically impossible
using other algorithms due to the exponential number of possible permutations
of such long strings.
