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ISBN 978-952-5726-09-1 (Print)
Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 054-057
Research for the Algorithm of Query to
Compressed XML Data
Guojia Yu 1 , Huizhong Qiu 2 , and Lin Tian 3
1
Scholl of Computer Science and Engineerin
University of Electronic Science and Technology of China, ChengDU, China
Email:yuguojia@foxmail.com
2
Scholl of Computer Science and Engineering
University of Electronic Science and Technology of China, ChengDU, China
Email: hzqiu@ uestc.edu.cn, ruan052@126.com
Abstract—Because XML data is increasingly becoming the
standard of transmission and distribution of Internet and
enterprise's data in a common format. Efficient algorithms
of compression and query in XML data can directly reduce
the cost of storage of data and shorten response time of
query. Studying in this aspect is widely promising. This
article proposed an equivalence relation on the basis of
characters of XML, and proved the rationality of the index
and the feasibility of query algorithm on this method, then
put forward a new query algorithm on the compressed
index. Finally, compared with XGrind that supports query
on the partial decompression of compressed XML data in
experiment. The efficiency of query on the compressed
index was significantly higher than Xgrind's in several sets
of data .
Index Terms—XML date; compressed index; query;
algorithm
I. INTRODUCTION
In this paper,build XML compressed index, and query
efficiently on this index. Complete it in three parts in
main:
First, code XML data of tags and attribute names with
dictionary, then use Huffman[1] coding to compress the
element values and attribute values.
Secondly, expand SAX generic events into another
events. And compress the original XML tree structure to
build a new compressed index, reduce greatly data
redundancy by the structured data itself.
Finally, query efficiently some data on the compressed
index.
II. BUILD THE COMPRESSED INDEX
A. Pre-Compression
The first step: use the dictionary of pre-compression to
encode XML data in the non-content nodes, scan the
DTD or Schema whose XML document would be
compressed, store the labels’ name and attributes’ name
into two dictionaries, and then the values of the
dictionary instead of these labels’ name and attributes’
name. After that,build the compressed indexed. In the
previous,article shows some concepts of terminology:
1 The same name item: the same name of tags basing
on the same parent node compose the same name item.
2 Different chain: the first element in all of the same
items based on the same parent node composed of the
different chain.
3Repetition rate of XML data: (the number of lable
elements of XML ― the number of the same name item)
/ the number of lable elements of XML.
4 The judgement event: combine two adjacent events
(that’s geniric events)of SAX,then expand API of the
event-driven SAX parser to a judgement event
B. TP Equivalence Relations
Illuminationed by the indexs of APEX[2], Fabric[3],
XQueC[4], XBZip[5] and other methods, this article will
convert the tree structure of XML to another index who
could guarantee to support efficient query. So introduce a
TP equivalence relations (tree to tree-graph)with two
structures can be interchangeable, that is isomorphic, as
follows.
Figure 1. XML document tree structure
[Definition 1] TP equivalence relation: given a tree G:
G ( V , E)
, where V is the set of nodes in G, E is the set of
edges in G. Convert G, you can get another form of treegraph
G '(
V ', E'
) . Based on G and G',we can define a
binary relation R.
If R satisfies the following conditions, that R is a TP
equivalence relations of G and G':
1) any node u in G has exactly the same and unique
corresponding node u' in G'.
2) If there is a node in G, whose child pointer p point
to the p 1
-the first child on the left, that they have a
p→
α p
relationship 1, then, in G' there must also exist a
corresponding element of q, which child pointer point to
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