(untitled) (International Journal of Food and Nutritional Sciences, 2019)
The purpose of this study is to discuss the efficient approximation string search in vast databases. We focus on range queries enhanced by string similarity search in both Euclidean space and road networks. We present an approximation solution in Euclidean space, the MHR-tree, which embeds min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings in the index node’s sub-tree. We also explain how to estimate the selectivity of a range query in a big database in Euclidean space and provide a unique adaptive technique for this purpose. For queries on road networks, we offer RSASSOL, a unique precise approach that greatly outperforms the baseline algorithm in practise. The RSASSOL combines q-gram-based, and pruning based on reference nodes and inverted lists