  Similarity-preserving hashing is a core technique for fast similarity
searches, and it randomly maps data points in a metric space to strings of
discrete symbols (i.e., sketches) in the Hamming space. While traditional
hashing techniques produce binary sketches, recent ones produce integer
sketches for preserving various similarity measures. However, most similarity
search methods are designed for binary sketches and inefficient for integer
sketches. Moreover, most methods are either inapplicable or inefficient for
dynamic datasets, although modern real-world datasets are updated over time. We
propose dynamic filter trie (DyFT), a dynamic similarity search method for both
binary and integer sketches. An extensive experimental analysis using large
real-world datasets shows that DyFT performs superiorly with respect to
scalability, time performance, and memory efficiency. For example, on a huge
dataset of 216 million data points, DyFT performs a similarity search 6,000
times faster than a state-of-the-art method while reducing to one-thirteenth in
memory.
