Towards a complete perspective on labeled tree indexing: New size bounds, efficient constructions, and beyond

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A labeled tree (or a trie) is a natural generalization of a string which can also be seen as a compact representation of a set of strings. This paper considers the labeled tree indexing problem, and provides a number of new results on space bound analysis and on algorithms for efficient construction and pattern matching queries. Kosaraju [FOCS 1989] was the first to consider the labeled tree indexing problem and he proposed the suffix tree for a backward trie, where the strings in the trie are read in the leaf-to-root direction. In contrast to a backward trie, we call an ordinary trie as a forward trie. Despite a few follow-up works after Kosaraju’s paper, indexing forward/backward tries is not well understood yet. In this paper, we show a full perspective on the sizes of indexing structures such as suffix trees, DAWGs, CDAWGs, suffix arrays, affix trees, affix arrays for forward and backward tries. Some of them take O(n) space in the size n of the input trie, while the others can occupy O(n2) space in the worst case. In particular, we show that the size of the DAWG for a forward trie with n nodes is Ω(σn), where σ is the number of distinct characters in the trie. This becomes Ω(n2) for an alphabet of size σ =Θ(n). Still, we show that there is a compact O(n)-space implicit representation of the DAWG for a forward trie, whose space requirement is independent of the alphabet size. This compact representation allows for simulating each DAWG edge traversal in O(log σ) time, and can be constructed in O(n) time and space over any integer alphabet of size O(n). In addition, this readily extends to the first indexing structure that permits bidirectional pattern searches over a trie within linear space in the input trie size. We also discuss the size of the DAWG built on a labeled DAG or on an acyclic DFA, and present a quadratic lower bound for its size.

ジャーナルJournal of information processing
出版ステータス出版済み - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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