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.

翻译：疫情常导致重大人员伤亡，并在公共卫生、旅游、经济等多方面对社会造成冲击。为遏制COVID-19等流行病传播，高效且有效的接触追踪至关重要，尤其在感染风险更高的室内场所。本文针对数字化人类室内活动的原始不确定室内定位数据，提出并研究了一种新型查询——室内接触查询（ICQ）。给定查询对象o（例如被确认为病毒携带者的人员），ICQ通过分析不确定室内定位数据，找出最可能长时间与o密切接触的对象。为处理ICQ，我们提出了一系列技术。首先，设计了一种增强型室内图模型，以组织ICQ所需的多类数据。其次，针对室内移动对象，提出了确定不确定区域及推导原始数据中缺失定位样本的方法。第三，提出了一种包含密切接触判定方法、搜索算法及加速策略的查询处理框架。我们在合成数据集与真实数据集上进行了广泛实验以评估所提方案，结果证明了其高效性与有效性。