An improvement to bottom-up tree pattern matching
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
BURG: fast optimal instruction selection and tree parsing
ACM SIGPLAN Notices
Efficient tree pattern matching (extended abstract): an aid to code generation
POPL '85 Proceedings of the 12th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Journal of the ACM (JACM)
A new algorithm for linear regular tree pattern matching
Theoretical Computer Science
A new method for compiler code generation
POPL '78 Proceedings of the 5th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Algorithms for pattern matching and discovery in RNA secondary structure
Theoretical Computer Science - Pattern discovery in the post genome
On regular tree languages and deterministic pushdown automata
Acta Informatica
Arbology: trees and pushdown automata
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Height-deterministic pushdown automata
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
Tree template matching in unranked ordered trees
Journal of Discrete Algorithms
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We consider the problem of tree template matching in ranked ordered trees, and propose a solution based on the bottom-up technique. Specifically, we transform the tree pattern matching problem to a string matching problem, by transforming the tree template and the subject tree to strings representing their postfix notation, and then use pushdown automata as the computational model. The method is analogous to the construction of string pattern matchers. The given tree template is preprocessed once, by constructing a nondeterministic pushdown automaton, which is then transformed to the equivalent deterministic one. Although we prove that the space required for preprocessing is exponential to the size of the tree template in the worst case, the space required for a specific class of tree templates is linear. The time required for the searching phase is linear to the size of the subject tree in both cases.