Unprecedented amounts of multidimensional array data are currently being generated in many fields. These multidimensional array data naturally and efficiently fit into the array data model, and many array management systems based on the array data model have appeared. Accordingly, the requirement for data exploration methods for large multidimensional array data has also increased. In this paper, we propose a method for efficient top-k subarray query processing in array databases, which is one of the most important query types for exploring multidimensional data. First, we define novel top-k query models for array databases: overlap-allowing and disjoint top-k subarray queries. Second, we propose a suite of top-k subarray query processing methods, called PPTS and extend them to distributed processing. Finally, we present the results of extensive experiments using real datasets from an array database, which show that our proposed methods outperform existing naïve methods.

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Recently, many video monitoring systems utilize deep learning technologies to recognize locations and trajectories of people in video data. In video monitoring systems, a fast discovery of human groups is an important task for several applications, for example, crime surveillance, contact tracing, and customer behavior analysis. To tackle the demand, we propose a group tracking method. First, we propose a spatial proximity definition and define a novel query type, a group tracking query that considers characteristics of video data. A group tracking query retrieves the groups that travel for more than a certain amount of video frame within a certain distance. We propose an efficient query processing method that exploits the spatio-temporal characteristics of groups. Through extensive experiments using real-world datasets, we verify the efficiency and effectiveness of our query definition and query processing method.INDEX TERMS Spatio-temporal query processing, spatial data management, spatial databases, video query processing, video monitoring systems. FIGURE 2. An example for perspective projection of a circle. When a circle (in Figure 2a) is observed by a camera that has a fixed position, it is projected into an ellipse as in Figure 2b.'search all the video segments with the length of 10 seconds in which two people A and B appear continuously'. For the temporal query processing, they use the modified clusteringand-intersection algorithm to be tolerant to object occlusion. However, their method considers all the objects in a frame as a single group.