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    Spatio-Temporal Aggregations in Trajectory Data Warehouses

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    In this paper we investigate some issues related to the design of a simple Data Warehouse (DW), storing several aggregate measures about trajectories of moving objects. First we discuss the loading phase of our DW which has to deal with overwhelming streams of trajectory observations, possibly produced at different rates, and arriving in an unpredictable and unbounded way. Then, we focus on the measure presence, the most complex measure stored in our DW. Such a measure returns the number of trajectories that lie in a spatial region during a given temporal interval. We devise a novel way to compute an approximate, but very accurate, presence aggregate function, which algebraically combines a bounded amount of measures stored in the base cells of the data cube

    A Computer Vision System for Monitoring Ice-Cream Freezers

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    In this paper, we describe a computer vision system aimed at monitoring the evolution of the content of a commercial ice-cream freezer. In particular, the system is able to detect the volume occupied by ice-creams in a basket and to track ice-cream sales. To this end, three modules have been developed performing the detection of the baskets and the products inside them, along with the tracking of the interactions with the freezer to take/drop products. The system comprises four cameras connected to an embedded mini-computer able to communicate with a telemetry system that sends information about the freezer context. Our proposed methods achieve promising results for the basket detection and the product tracking (accuracy around 70–80%) and good results in the volume estimation
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