kSubstrat

kSubstrat is a framework and set of techniques for processing k-length substrings, or k-substrings, within strings, sequences, and logs. It generalizes the concept of k-grams by focusing on scalable extraction, indexing, and querying of all substrings of fixed length k.

The core components include a k-substring extractor, which slides a window of length k across a sequence to generate each substring, and an index that stores frequency, positions, or multiplicities. Data structures used in kSubstrat implementations range from tries and suffix trees/arrays to de Bruijn graphs and compact hash-based structures. Space-efficient variants may employ Bloom filters or other probabilistic structures to manage large datasets.

Operations supported by kSubstrat typically include exact substring matching, frequency counting, and existence queries, with extensions for approximate matching that allow a limited number of mismatches or edits. Some designs also enable streaming processing to handle real-time data.

Applications span multiple domains. In genome analysis, k-substrings correspond to k-mers used in assembly and comparison. In text mining and plagiarism detection, they enable rapid substring-based similarity checks. Other uses include motif discovery, data compression, and anomaly detection in system logs.

Performance and limitations: extraction is generally linear in the input length, while memory usage depends on the number of distinct k-substrings and the chosen data structures. Large alphabets or large k increase the state space, and approximate querying can add overhead. Distributed or streaming variants help scale to big data.

Related concepts include k-mer, n-gram, suffix array, and de Bruijn graph. See also: k-mer, n-gram, substring.