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The routing table matching on longest prefix is divided into common prefix match and feature prefix match in this routing algorithm.
To achieve an efficient mapping onto this type of architecture we introduce a parallel suffix tree construction algorithm that makes use of a new data structure called the common prefix suffix tree.
In this article, we are presenting an algorithm that modifies the suffix array and the Longest Common Prefix (LCP) array when the text is edited (insertion, substitution or deletion of a letter or a factor).
After all, it is wrong to punch anyone except a puncher, and our language even has special words — like "retaliation" and "retribution" and "revenge" — whose common prefix is meant to remind us that a punch thrown second is legally and morally different than a punch thrown first.
Thus, we generate rules within each common prefix sub-tree.
To improve lookup performance, a routing table is used, which is populated with nodes that share a common prefix.
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Common prefixes: Common antonym prefixes include UN-, IM-, IM-, DE- DIS-, MIS-, and NON-, with IG- a less common antonym prefix.
Another dictionary was used to match common prefixes and postfixes that appear in specific groups of chemicals like organic chemistry.
The most common prefixes are na-, o-, po-, s-, u-, vy-, z- and za-.
One example of a compressed suffix tree implementation is CST++ [ 18], which uses a new approach to compress the longest common prefix-array and achieves, depending on the sampling rates of the (inverse) suffix array, an usual space overhead of (4−6)∗ n bits.
Neighboring suffixes share the longest common prefixes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com