  Many services today massively and continuously produce log files of different
and varying formats. These logs are important since they contain information
about the application activities, which is necessary for improvements by
analyzing the behavior and maintaining the security and stability of the
system. It is a common practice to store log files in a compressed form to
reduce the sheer size of these files. A compression algorithm identifies
frequent patterns in a log file to remove redundant information. This work
presents an approach to detect frequent patterns in textual data that can be
simultaneously registered during the file compression process with low
consumption of resources. The log file can be visualized with the possibility
to explore the extracted patterns using metrics based on such properties as
frequency, length and root prefixes of the acquired pattern. This allows an
analyst to gain the relevant insights more efficiently reducing the need for
manual labor-intensive inspection in the log data. The extension of the
implemented dictionary-based compression algorithm has the advantage of
recognizing patterns in log files of any format and eliminates the need to
manually perform preparation for any preprocessing of log files.
