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.