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I know that similar computational problems use indexing and vector-space representation but how would you build an index of TiBs of almost-random data that makes it faster to find the strictly closest match of an arbitrarily long sequence? I can think of some heuristics, such as bitmapping every occurrence of any 8-pair sequence across each kibibit in the list. A query search would then add the bitmaps of all 8-pair sequences within the query including ones with up to 2 errors, and using the resulting map to find "hotspots" to be checked with brute force. This will decrease the computation and storage access per query but drastically increase the storage size, which is already hard to manage.

However, efficient fuzzy string matching in giant datasets is an interesting problem that computer scientists must have encountered before. Can you find a good paper that works well with random, non-delimited data instead of just using the approach of word-based indices for human languages like Lucene and OpenFTS?