XML Compression

XML Compression

Chin-Wan Chung, Myung-Jae Park, Jihyun Lee
DOI: 10.4018/978-1-61520-727-5.ch003
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To effectively reduce the redundancy and verbosity of XML data, various studies for XML compression have been conducted. Especially, XML data management systems and applications require the support of direct query processing and update on compressed XML data, the stream based compression/decompression, and the reduction of the size of the compressed data. In order to fully support the various aspects of XML compression, existing XML compression techniques should be carefully examined and the additional requirements for XML compression techniques should be considered. In this chapter, the authors first classify existing representative XML compression techniques according to their characteristics. Second, they explain the details of XML specific compression techniques. Third, they summarize the performance of the compression techniques in terms of the compression ratio and the compression and decompression time. Lastly, they present some future research directions.
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XML (eXtensible Markup Language) (Bray et al., 2008) is a standardized markup language to represent and exchange data on the Web. Due to the proliferation of the Web, the usage of XML has tremendously increased. As a result, more data are produced as XML data and the size of XML data also increases. In order to efficiently manage such XML data, various types of researches on issues such as XML indexing, XML query processing, and XML storage have been conducted. Moreover, to effectively manage large sized XML data, the compression on XML data was required.

Data compression is the process of encoding data with a size smaller than that of the original data using specific encoding methods. As a result, data compression provides several important advantages. First, the storage size for compressed data is reduced compared with that for original data. Second, the network bandwidth can be saved with the reduced data size since much more data can be transferred through the network within a given period of time. Lastly, the performance of query processing can be improved since the memory is efficiently utilized and the required number of disk I/Os is reduced.

According to those advantages of the data compression, the compression of XML data has been interested in various areas such as archiving, query processing, data dissemination and so on. Since an XML document is generally a text file, general text compression techniques such as gzip (Gailly & Adler, 2007) and bzip2 (Seward, 2008) can be employed to compress the XML document. However, an XML document is distinguished from a general plain text by the existence of the semantic structure in the XML document. Thus, various researches for XML specific compression have been conducted to effectively solve the redundancy and verbosity problems of XML data.

The XML specific compressors take advantage of the structure-awareness to improve the performance of the compression. In general, the structure of an XML document, which can be modeled as a tree composed of redundant elements and attributes, has a high regularity such that similar sub-trees repeatedly appear throughout the document. Also, data values enclosed by the same element have the same data type or are similar to each other. The regularity of data increases the compression ratio. Therefore, the XML specific techniques compress the syntactically or semantically partitioned structure and content using different encoding models to sufficiently take advantage of the local homogeneity. For the XML compression, the high compression ratio is an important goal of the XML compression. In addition, since XML data can be frequently queried and updated, the direct evaluation of queries and the direct update on compressed XML data should be possible. Since XML data are frequently exchanged in the Internet, the stream based compression/decompression should also be considered. Until now, most studies on the XML compression have focused on the achievement of high compression ratio and the direct query evaluation on compressed XML data. In order to fully support the above aspects of XML compression, existing XML compression techniques should be carefully examined and the additional requirements for XML compression techniques should be addressed.

The objective of this chapter is to provide a better understanding on relevant theoretical frameworks and an up-to-date research trend of the XML compression. To achieve this goal, this chapter contains the following contents.

First, various existing XML compression techniques are classified according to their characteristics. The classified categories are schema-dependent compression and schema-independent compression, non-queriable compression and queriable compression, and homomorphic compression and non-homomorphic compression. The characteristics of each category are introduced in detail.

Second, representative XML specific compression techniques including the latest ones such as XMill (Liefke & Suciu, 2000), XMLPPM (Cheney, 2001), XAUST (Hariharan & Shankar, 2006), XGRIND (Tolani & Haritsa, 2002), XQzip (Ng & Cheng, 2004), XPRESS (Min et al., 2003), XBzipIndex (Ferragina et al., 2006), XCQ (Ng et al., 2006b), XQueC (Arion et al., 2007), and ISX (Wong et al., 2007) are presented. Also, the compression performance of those compression techniques is summarized in terms of compression ratio, compression time, and decompression time. Furthermore, based on their characteristics and experimental evaluation, appropriate XML compression techniques for different environments are recommended.

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