Extensible Markup Language (XML) Compression Tool

XML is becoming the dominant means of universally communicating data designed to be processed by machines in networks, much the same way as HTML has become the dominant means of communicating data designed to be processed by human beings. However, like HTML, the human-readable source format of XML leads to large files with many redundancies. Therefore, although universal, XML documents are not the most efficient way to transmit and store data. In the field, such efficient transmission and storage is vital to the data-gathering effectiveness of a unit. Because the XML format is universal, it cannot be abandoned, thus making XML compression necessary. This project consists of the following objectives designed to achieve efficient XML compression. Firstly, design and develop a novel and efficient XML compressor (and decoder) based on context analysis and machine learning. Secondly, design and develop a preliminary codec using this compressor/decoder that will in addition compress any still or video imagery that might be included in the XML data stream. The anticipated result of this Phase I research is (1) the design of a superior intelligence-based, adaptive XML compression method and associated algorithms (2) the development of a preliminary XML compression tool based on the algorithms designed. The feasibility of this nascent compressor will be demonstrated and performance will be scientifically extrapolated in order predict the efficiency of the compressor to be developed in Phase II.