  Entity Resolution suffers from quadratic time complexity. To increase its
time efficiency, three kinds of filtering techniques are typically used for
restricting its search space: (i) blocking workflows, which group together
entity profiles with identical or similar signatures, (ii) string similarity
join algorithms, which quickly detect entities more similar than a threshold,
and (iii) nearest-neighbor methods, which convert every entity profile into a
vector and quickly detect the closest entities according to the specified
distance function. Numerous methods have been proposed for each type, but the
literature lacks a comparative analysis of their relative performance. As we
show in this work, this is a non-trivial task, due to the significant impact of
configuration parameters on the performance of each filtering technique. We
perform the first systematic experimental study that investigates the relative
performance of the main methods per type over 10 real-world datasets. For each
method, we consider a plethora of parameter configurations, optimizing it with
respect to recall and precision. For each dataset, we consider both
schema-agnostic and schema-based settings. The experimental results provide
novel insights into the effectiveness and time efficiency of the considered
techniques, demonstrating the superiority of blocking workflows and string
similarity joins.
