A Dynamic Compressed Self-Index for Highly Repetitive Text Collections
Takaaki Nishimoto, Yoshimasa Takabatake, Yasuo Tabei · 2018
We present a novel compressed dynamic self-index for highly repetitive text collections. Signature encoding, an existing self-index of this type, has a large disadvantage of slow pattern search for short patterns. We obtain faster pattern search by leveraging the idea behind a truncated suffix tree (TST) to develop the first compressed dynamic self-index, called the TST-index, that supports not only fast pattern search but also dynamic update operations for highly repetitive texts. Experiments with a benchmark dataset show that the pattern search performance of the TST-index is significantly improved.