Compressed Xml Database Query Processing Method

Recently, XML has already been the standard of data express, exchange and storage, it brings a new domain of database development, but because of its half structural characteristic and the special Path Expression Query way of the XML data, which is so different from the relational database query, then restrict XML use the DBMS deeply. Because of that, we have to face the biggest fault of XML, which is its data redundance, it is redundant in two aspects: structural redundancy and content redundancy, that is it is repeated both in path and element. Redudency not only enhance the query processing I/O time, but also a waste of the storage space, and in the end, it debase the efficiency of XML, which lead the compression of XML a necessary work. But the query processing and other operation should be done after the decompression of XML document, which is also a waste, so how to query efficiently in compressed XML document is becoming the most important issue of XML study, and the compressor which support the directly query on the compressed data is becoming more and more important, it not only can save the memory usage space, but also can accelerate the query processing time, and at the same time get a better compression rate.The paper eliminates redundant information of XML in structure and content aspects, and according to the drawbacks of the XML compressor which are already known, the thesis proposed two XML compress algorithms which support the directly query processing the compressed XML documents, finally, the paper gives an efficient XML compression model which is solve the problems which are mentioned before, compares with other methods and makes many analysis, which show efficient in some domain.At last, according to the query processing XML compression model,, the thesis implements a XML Compression and Query prototype system and compare the compression time, compression rate and query processing time, and then analyses the result which is showed that my XML compression methods are both balance the compression rate and query time, and at the same time, they both support the query directly after the compression, and we enhance the compression effect to some extent.