  Pandemics often cause dramatic losses of human lives and impact our societies
in many aspects such as public health, tourism, and economy. To contain the
spread of an epidemic like COVID-19, efficient and effective contact tracing is
important, especially in indoor venues where the risk of infection is higher.
In this work, we formulate and study a novel query called Indoor Contact Query
(ICQ) over raw, uncertain indoor positioning data that digitalizes people's
movements indoors. Given a query object o, e.g., a person confirmed to be a
virus carrier, an ICQ analyzes uncertain indoor positioning data to find
objects that most likely had close contact with o for a long period of time. To
process ICQ, we propose a set of techniques. First, we design an enhanced
indoor graph model to organize different types of data necessary for ICQ.
Second, for indoor moving objects, we devise methods to determine uncertain
regions and to derive positioning samples missing in the raw data. Third, we
propose a query processing framework with a close contact determination method,
a search algorithm, and the acceleration strategies. We conduct extensive
experiments on synthetic and real datasets to evaluate our proposals. The
results demonstrate the efficiency and effectiveness of our proposals.
