Optimal Dynamic Strings

In this paper we study the fundamental problem of maintaining a dynamic collection of strings under the following operations: concat - concatenates two strings, split - splits a string into two at a given position, compare - finds the lexicographical order (less, equal, greater) between two strings, LCP - calculates the longest common prefix of two strings. We present an efficient data structure for this problem, where an update requires only $O(\log n)$ worst-case time with high probability, with $n$ being the total length of all strings in the collection, and a query takes constant worst-case time. On the lower bound side, we prove that even if the only possible query is checking equality of two strings, either updates or queries take amortized $\Omega(\log n)$ time; hence our implementation is optimal. Such operations can be used as a basic building block to solve other string problems. We provide two examples. First, we can augment our data structure to provide pattern matching queries that may locate occurrences of a specified pattern $p$ in the strings in our collection in optimal $O(|p|)$ time, at the expense of increasing update time to $O(\log² n)$. Second, we show how to maintain a history of an edited text, processing updates in $O(\log t \log \log t)$ time, where $t$ is the number of edits, and how to support pattern matching queries against the whole history in $O(|p| \log t \log \log t)$ time. Finally, we note that our data structure can be applied to test dynamic tree isomorphism and to compare strings generated by dynamic straight-line grammars.