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Efficient multi-word parameterized matching on compressed text

Authors: Rajesh Prasad; Rama Garg;

Efficient multi-word parameterized matching on compressed text

Abstract

Searching set of patterns {P 1 , P 2 , P 3 , ….P r }, r≥1, inside body of a text T[1…n] is called multi-pattern matching problem. This matching is said to be parameterized match (p-match), if one can be transformed into the other via some bijective mapping. It is mainly used in software maintenance, plagiarism detection and detecting isomorphism in a graph. In the compressed parameterized matching problem, our task is to find all the parameterized occurrences of a pattern (set of patterns) in the compressed text, without decompressing it. Compressing the text before matching reduces the size and minimizes the matching time also. In this paper, we develop an efficient algorithm for parameterized multi-word matching problem on the compressed text, where both patterns and text are compressed before actual matching is performed and pattern is treated as word. For compressing the pattern and text, we use efficient compression code: Word Based Tagged Code (WBTC) and bit-parallel algorithm is used for searching purpose. Experimental results show that our algorithm is up to three times faster than the search on the uncompressed text.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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