Space-economical partial gram indices for exact substring matching

Exact substring matching queries on large data collections can be answered using q-gram indices, that store for each occurring q-byte pattern an (ordered) posting list with the positions of all occurrences. Such gram indices are known to provide fast query response time and to allow the index to be created quickly even on huge disk-based datasets. Their main drawback is relatively large storage space, that is a constant multiple (typically >2) of the original data size, even when compression is used. In this work, we study methods to conserve the scalable creation time and efficient exact substring query properties of gram indices, while reducing storage space. To this end, we first propose a partial gram index based on a reduction from the problem of omitting indexed q-grams to the set cover problem. While this method is successful in reducing the size of the index, it generates false positives at query time, reducing efficiency. We then increase the accuracy of partial grams by splitting posting lists of frequent grams in a frequency-tuned set of signatures that take the bytes surrounding the grams into account. The resulting qs-gram scheme is tested on huge collections (up to 426GB) and is shown to achieve an almost 1:1 data:index size, and query performance even faster than normal gram methods, thanks to the reduced size and access cost.