1. Storing and Querying XML Documents Without Using Schema Information
Kanda Runapongsa
Department of Computer Engineering
Khon Kaen University, Thailand
Jignesh M. Patel
Department of EECS
University of Michigan, USA

2. Sample XML Data <bib> <book>
<author>John</author>
<publisher>ABC</publisher>
</book>
<article>
<author>Brown</author>
<author>Smith</author>
</article>
</bib>

3. Motivation The amount of XML data is increasing rapidly
Enterprise application integration
B2B interchange
Web services
Efficient tools for managing large XML data sets are urgently needed
Solutions range from using native XML DBMSs to relational DBMSs

4. Pros & Cons of Using an RDBMS
Advantages of using an RDBMS
Well developed query optimization techniques
Effective storage and indexing mechanisms
Scalability, parallelism, and distributed processing
Concurrency and recovery
Disadvantages of using an RDBMS
Need to transform between XML data and relational data
Need to perform multiple joins between tables
This work: try to improve the performance of an RDBMS

5. Queries on XML Typical inquiries on XML are containment queries
Direct containment query: query that tests whether element c is a child of element p
Example: p/c
Indirect containment query: query that tests whether element d is a descendant of element a
Example: a//d
How should we process these queries?

6. XML Query Processing (Using Schema)*
book
article +
author
publisher
bib
Using a DTD graph
which represents the
structure of the DTD
Nodes corresponds to
Elements
Example: book
Attributes
Operators
Example: *

7. XML Query Processing (Using Schema)
Legend
Rules for mapping nodes to table/attributes in an RDBMS
1. Create tables for these following nodes:
(C1): no incoming link
(C2): below a ‘*’ node or a ‘+’ node
2. Inline all remaining nodes as table attributes
bib
table
attribute * *
book
article + +
author
publisher

8. XML Query Processing (Not Using Schema)
bib
(begin, end, level)
Q: book/author
A: (3,5,3)
Containment query:
d is contained in a iff abegin < dbegin &&
dend < aend
d is directly contained in a if dlevel = alevel – 1
book/author
2 < 3 && 5 < 9 &&
3 = 2 - 1
(1,18,1)
(10,17,2)
(2,9,2)
book
article
(3,5,3)
(11,13,3)
author
author
(6,8,3)
(14,16,3)
author
publisher
John
Brown
Smith
ABC
(4,4,4)
(12,12,4)
(15,15,4)
(7,7,4)

9. This Proposed Solution (PAID)
Use a numbering scheme (begin, end, level) to solve the direct and indirect containment queries because it is applicable even when there is no schema information
Store the path information of the node to solve a long path query, such as a/b/c/d/e
Store the position of the parent node for each node to quickly establish parent-child relationship (direct containment) between any two given nodes

10. Storing Node Information in Tables
element
<author>
docID term pathID begin end level parentID 1
author 3 5 2 6 11 13 10 14 16
(3,5,3)
(11,13,3)
(14,16,3)
John
text
term
docID
wordno
level
parentID
John 1 4 3
(4,4,4)
path
pathExp pathID
/bib/book/author 3
/bib/article/author 6

11. Other Mapping Approaches
The BEL approach [ZND+01]
Has begin, end, level information
But no path and parent ID information
Store a single word on each tuple
The BELP approach [SYU99]
Has begin, end, level information (but stored as float)
Has path information, but no parentID information
Store multiple words on each tuple

12. SQL Queries using PAID book/author select * from element b, element a
where b.term = ‘book’
and a.term = ‘author’
and b.docID = a.docID
and a.parentID = b.begin
bib//author
select *
from element b,
element a
where b.term = ‘bib’
and a.term = ‘author’
and b.docID = a.docID
and b.begin < a.begin
and a.end < b.end

13. SQL Queries using Other Approaches
BEL: book/author
select *
from element b,
element a
where b.term = ‘book’
and a.term = ‘author’
and b.docID = a.docID
and b.begin < a.begin
and a.end < b.end
and b.level = a.level - 1
BELP: book/author
select *
from element e,
path p
where e.pathID = p.pathID
and p.pathExp = ‘*book/author’

14. Experimental Setup: Platforms
Software
Apache Xerces C++ version 2.0 to parse the documents and generate the content of relations in different mapping approaches
Commercial RDBMS: IBM DB2 UDB 7.0
32 MB Buffer pool size
Hardware
1.2 GHz Pentium Celeron, 256 MB Memory
Windows XP

15. Experimental Setup: Data Set
Data set: the Shakespeare Plays XML documents
The size of a copy of the data set is 8 MB
To have the large size of the experimental data, use eight copies of the original Shakespeare data set. Thus, the total input data size is 64 MB
Each data set has 37 files and each file is about 0.2 MB

16. Experimental Setup: Workload
6 micro-benchmark queries
‘element’ contains ‘text’
Direct containment
Indirect containment
Examples
ACT/SCENE/SPEECH/LINE[contains(STAGEDIR, ‘Rising’)]
/PLAY[contains(TITLE,’Juliet’)]//ACT/SCENE/SPEECH[contains(LINE, ‘love’)][contains(SPEAKER, ‘ROMEO’)]

17. Experimental Results Query Execution Times (seconds) BEL BELP PAID QS1
24.92
30.70
0.03
QS2
81.39
18.46
10.29
QS3
367.39
836.00
30.99
QS4
10.67
23.35
1.42
QS5
580.40
952.09
56.61
QS6
3.54
0.11
0.01
The PAID approach outperforms other approaches
by several orders of magnitude

18. Experimental Results

19. Why Does PAID Perform Better?
1) PAID uses the parentID attribute to quickly find the parent nodes
2) PAID uses the path information to reduce the number of join operations in long path queries
When only using the ‘begin, end, level’, the number of joins is proportional to the number of elements on the path
3) PAID uses the index on the value attribute to quickly retrieve the nodes that satisfy with the value-based predicates

20. Conclusions We can use an RDBMS for storing and querying XML data
Pros: Has technology that has been developed for several decades (optimization, concurrency control, and recovery)
Cons: not well-tuned for containment queries
Performance on an RDBMS could be better if we encode more information
Parent ID
Path information

21. References
[ZND+01] C. Zhang, J. Naughton, D. DeWitt, Q. Luo, and G. Lohman, “On Supporting Containment Queries in Relational Database Management Systems”, In SIGMOD 2001
[SYU+99] T. Shimura, M. Yoshikawa, and S. Uemura, “Storage and Retrieval of XML Documents Using Object-Relational Databases”, In International Conference on Database and Expert Systems Applications 1999