Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Research on Parallel Query of XML Stream Data Based on Pushdown Transducers

Authors
Hongliang Xie, Husheng Liao, Hongyu Gao
Corresponding Author
Hongliang Xie
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.98How to use a DOI?
Keywords
XML stream; XPath; ordered tree pattern; pushdown transducer; data parallel
Abstract
the social networking, network monitoring and financial applications have a need to query high rate streaming of XML data, but previous methods for executing XPath queries on streaming XML data have not kept pace with multicore CPUs. Data parallel query methods can improve processing efficiency by using multi-core CPU. Push down transducer as a special type of automaton, it not only can be used to handle XML data, and its internal stack structure can preserve the important information in the processing, so it can combine with the data parallel querying to improve the processing efficiency further. In order to improve the efficiency, transfers XPath query pattern into a series of ordered subquery which its result are valid and has easier logic.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.98How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hongliang Xie
AU  - Husheng Liao
AU  - Hongyu Gao
PY  - 2017/06
DA  - 2017/06
TI  - Research on Parallel Query of XML Stream Data Based on Pushdown Transducers
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 433
EP  - 437
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.98
DO  - https://doi.org/10.2991/caai-17.2017.98
ID  - Xie2017/06
ER  -