  In this paper, we study the problem of spatial link discovery (LD), focusing
primarily on topological and proximity relations between spatial objects. The
problem is timely due to the large number of sources that generate spatial
data, including streaming sources (e.g., surveillance of moving objects) but
also archival sources (such as static areas of interest). To address the
problem of integrating data from such diverse sources, link discovery
techniques are required to identify various spatial relations efficiently.
Existing approaches typically adopt the filter and refine methodology by
exploiting a blocking technique for effective filtering.
  In this paper, we present a new spatial LD technique, called MaskLink, that
improves the effectiveness of the filtering step. We show that MaskLink
outperforms the state-of-the-art algorithm for link discovery of topological
relations, while also addressing some of its limitations, such as applicability
for streaming data, low memory requirements, and parallelization. Furthermore,
we show that MaskLink can be extended and generalized to the case of
proximity-based link discovery, which has not been studied before for spatial
data.
  Our empirical study demonstrates the superiority of MaskLink against the
state-of-the-art in the case of topological relations, and its performance gain
compared to a baseline technique in the case of proximity-based LD.
