High performance geography markup language (GML) multi-document stream compression information processing method

Abstract

The invention relates to a high performance geography markup language (GML) multi-document stream compression information processing method, which includes the following steps that 1 an input device inputs GML multi-document data to a GML document analyzer; 2 the GML document analyzer uses simple API for XML (SAX) to analyze the multi-document data; 3 an event processing module compresses structural information in the multi-document data through a dynamic structure compression algorithm to be stored in a structural container, spatial data in the multi-document data are compressed through a spatial data incremental compression algorithm to be stored in a spatial data container, and attribute data, text value and current document information data in the multi-document data are stored in an attribute container, a text container and a document information container respectively; and 4 data in all containers are compressed through a text compression tool Gzip as a background compression tool to obtain an integrated independent compression document of the document. Compared with the prior art, the high performance GML multi-document stream compression information processing method has the advantages of being high in compression rate, short in compressing time and uncompressing time, convenient to use and the like.

Description

technical field [0001] The invention relates to a compressed information processing method, in particular to a high-performance GML multi-document stream compressed information processing method. Background technique [0002] With the continuous improvement of GIS (Geography Information Systems) technology and the continuous promotion and popularity of GIS applications, it has become a supporting platform for more and more spatial geographic information systems in recent years. With the rapid development of the Internet and the widespread use of the Web, GIS has gradually evolved from a centralized system used by professionals to a distributed and networked massive information system. To enable people to use mobile phones or other mobile personal devices to access any spatial information at any time and any place. [0003] However, the existing spatial information processing methods and technologies still face many challenges. Because most of the existing GIS systems are i...

AI Technical Summary

Problems solved by technology

[0009] 1) Regardless of whether it is the compression end or the decompression end of the data stream, the system cannot store all the data of the data stream, so only part of the data can be cached during the compression process, and the data in the data stream changes over time increased, so the compression method must be able to process the data stream quickly;

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