The vision of the human genome project was born in the early 1980s. In 1990, a decade later, it was launched officially in the U.S. Another decade later, a first draft of the human genome was announced in 2000. In the same period, costs for computer hardware dropped and capacities of main memory and storage systems underwent an exponential growth. Today, DNA sequencing and genome analysis are turned into reality, e.g. in cancer treatments, analyzing gigabytes up to terabytes of data. With the upcoming trend of e-Health, i.e. supporting medical processes by electronic devices and communications, the amount of data produced keeps growing also in other fields of the medical sector. Patient records and clinical documentations are being digitalized, self-tracking fitness devices and applications are getting more popular. However, analysis of this kind of data nowadays is a time-consuming, manual task or even not possible at all because it lacks appropriate tools. Data management and analysis comes with various challenges, such as huge storage requirements, traditional scanning algorithms are based on reading sequences of characters from files, processing of operational data in databases is only rarely considered, parallelization of processing, etc.