Master/Bachelor Thesis: Efficient Comp. of XML & JSON Event Log Records Using Domain-Specific Knwl

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About this opportunity:

The exponential growth of digital data necessitates efficient storage solutions, particularly in the context of event log records generated by various applications. Traditional compression algorithms, though effective, often fail to leverage the inherent structure and patterns within log data, especially when dealing with XML or JSON formats. This thesis proposes a domain-specific approach to compressing large volumes of event log records by exploiting the known or static structure of the data and patterns identified through observation. By tailoring the compression algorithm to the unique characteristics of these log records, we aim to achieve significant improvements in compression efficiency compared to standard algorithms

What you will do:

This thesis explores a domain-specific approach to compressing large volumes of event log records, particularly in XML and JSON formats, by leveraging their inherent structure and patterns. Traditional compression algorithms, such as gzip and bzip2, often do not fully exploit the repetitive and structured nature of such log data. By analyzing common structures, static elements, and recurring sequences within these formats, we aim to develop a tailored compression algorithm that significantly enhances efficiency. The proposed method will be evaluated against standard algorithms to compare compression ratios, speed, and resource usage. Additionally, the scalability and feasibility of integrating this domain-specific algorithm with existing log management systems will be assessed. The research seeks to demonstrate that utilizing domain-specific knowledge can lead to substantial improvements in data storage and management, paving the way for more efficient handling of large-scale event log records.

The skills you bring:

This project aims at bachelors or masters students in co