A high-performance approach to string similarity using most frequent K characters

The amount of available data has been growing significantly over the last decades. Thus, linking entries across heterogeneous data sources such as databases or knowledge bases, becomes an increasingly difficult problem, in particular w.r.t. the runtime of these tasks. Consequently, it is of utmost importance to provide time-efficient approaches for similarity joins in the Web of Data. While a number of scalable approaches have been developed for various measures, the Most Frequent k Characters (MFKC) measure has not been tackled in previous works. We hence present a sequence of filters that allow discarding comparisons when executing bounded similarity computations without losing recall. Therewith, we can reduce the runtime of bounded similarity computations by approximately 70%. Our experiments with a single-threaded, a parallel and a GPU implementation of our filters suggest that our approach scales well even when dealing with millions of potential comparisons.