  Recent advances in social and mobile technology have enabled an abundance of
digital traces (in the form of mobile check-ins, association of mobile devices
to specific WiFi hotspots, etc.) revealing the physical presence history of
diverse sets of entities (e.g., humans, devices, and vehicles). One challenging
yet important task is to identify k entities that are most closely associated
with a given query entity based on their digital traces. We propose a suite of
indexing techniques and algorithms to enable fast query processing for this
problem at scale. We first define a generic family of functions measuring the
association between entities, and then propose algorithms to transform digital
traces into a lower-dimensional space for more efficient computation. We
subsequently design a hierarchical indexing structure to organize entities in a
way that closely associated entities tend to appear together. We then develop
algorithms to process top-k queries utilizing the index. We theoretically
analyze the pruning effectiveness of the proposed methods based on a mobility
model which we propose and validate in real life situations. Finally, we
conduct extensive experiments on both synthetic and real datasets at scale,
evaluating the performance of our techniques both analytically and
experimentally, confirming the effectiveness and superiority of our approach
over other applicable approaches across a variety of parameter settings and
datasets.
