The techniques developed in the paper can be applied to solve diversity maximization problems in other string-related domains, such as finding diverse substrings or diverse patterns in a set of sequences. Here's how the techniques can be adapted for these scenarios:
Diverse Substrings: By representing a set of substrings as a language accepted by a directed acyclic graph (DAG), similar to how longest common subsequences are represented, the algorithms can be modified to find diverse substrings based on different distance measures. The DP-table structures and recurrence relations would need to be adjusted to handle substring comparisons instead of full strings.
Diverse Patterns: In the context of finding diverse patterns in a set of sequences, the algorithms can be tailored to identify unique motifs or sequences that appear frequently across the dataset. The color-coding technique and dynamic programming approach can be applied to maximize the diversity of patterns while considering various distance metrics to measure their similarity.
By customizing the algorithms to suit the specific requirements of finding diverse substrings or patterns, researchers can efficiently analyze and extract meaningful information from sets of sequences in diverse applications, ranging from text mining to biological sequence analysis.