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Recently, randomly mapping vectorial data to strings of discrete symbols(i.e., sketches) for fast and space-efficient similarity searches has becomepopular. Such random mapping is called similarity-preserving hashing andapproximates a similarity metric by using the Hamming distance. Although manyefficient similarity searches have been proposed, most of them are designed forbinary sketches. Similarity searches on integer sketches are in their infancy.In this paper, we present a novel space-efficient trie named $b$-bit sketchtrie on integer sketches for scalable similarity searches by leveraging theidea behind succinct data structures (i.e., space-efficient data structureswhile supporting various data operations in the compressed format) and afavorable property of integer sketches as fixed-length strings. Ourexperimental results obtained using real-world datasets show that a trie-basedindex is built from integer sketches and efficiently performs similaritysearches on the index by pruning useless portions of the search space, whichgreatly improves the search time and space-efficiency of the similarity search.The experimental results show that our similarity search is at most one orderof magnitude faster than state-of-the-art similarity searches. Besides, ourmethod needs only 10 GiB of memory on a billion-scale database, whilestate-of-the-art similarity searches need 29 GiB of memory.

$b$-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches