DocumentCode :
37682
Title :
Multi-Core Processing of XML Twig Patterns
Author :
Shnaiderman, Lila ; Shmueli, Oded
Author_Institution :
Comput. Sci. Dept., Technion - Israel Inst. of Technol., Haifa, Israel
Volume :
27
Issue :
4
fYear :
2015
fDate :
April 1 2015
Firstpage :
1057
Lastpage :
1070
Abstract :
XML is based on a tree-structured data model. Naturally, the most popular XML querying language (XPath) uses patterns of selection predicates, on multiple elements related by a tree structure, which often may be abstracted by twig patterns. Finding all occurrences of such a twig pattern in an XML database is a basic operation for XML query processing. We present the parallel path stack algorithm (PPS) and the parallel twig stack algorithm (PTS). PPS and PTS are novel and efficient algorithms for matching XML query twig patterns in a parallel multi-threaded computing platform. PPS and PTS are based on the PathStack and TwigStack algorithms [1]. These algorithms employ a sophisticated search technique for limiting processing to specific subtrees. We conducted extensive experimentation with PPS and PTS. We compared PPS and PTS to the standard (sequential) PathStack and TwigStack algorithms in terms of run time (to completion). We checked their performance for varying numbers of threads. Experimental results indicate that using PPS and PTS significantly reduces the running time of queries in comparison with the PathStack/TwigStack algorithm (up to 44 times faster for DBLP queries and up to 22 times faster for XMark queries).
Keywords :
XML; data mining; parallel processing; query languages; tree data structures; PathStack algorithm; TwigStack algorithm; XML database; XML querying language; XML twig pattern; XPath; multicore processing; parallel multithreaded computing; parallel path stack algorithm; parallel twig stack algorithm; tree-structured data model; Databases; Instruction sets; Parallel algorithms; Partitioning algorithms; Pattern matching; Vegetation; XML; Query processing; concurrency;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2014.2349907
Filename :
6880848
Link To Document :
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