Finding frequently visited indoor POIs using symbolic indoor tracking data

Indoor tracking data is being amassed due to the deployment of indoor positioning technologies. Analysing such data discloses useful insights that are otherwise hard to obtain. For example, by studying tracking data from an airport, we can identify the shops and restaurants that are most popular among passengers. In this paper, we study two query types for finding frequently visited Points of Interest (POIs) from symbolic indoor tracking data. The snapshot query finds those POIs that were most frequently visited at a given time point, whereas the interval query finds such POIs for a given time interval. A typical example of symbolic tracking is RFID-based tracking, where an object with an RFID tag is detected by an RFID reader when the object is in the reader's detection range. A symbolic indoor tracking system deploys a limited number of proximity detection devices, like RFID readers, at preselected locations, covering only part of the host indoor space. Consequently, symbolic tracking data is inherently uncertain and only enables the discrete capture of the trajectories of indoor moving objects in terms of coarse regions. We provide uncertainty analyses of the data in relation to the two kinds of queries. The outcomes of the analyses enable us to design processing algorithms for both query types. An experimental evaluation with both real and synthetic data suggests that the framework and algorithms enable efficient and scalable query processing.