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Efficiently querying multiple spatial datasets is a challenging task in geoscience. The majority of spatial processing techniques use minimum bounding box (MBB) to approximate neighbouring spatial objects and and place them adjacent in the spatial index. However, due to the existence of redundant space in MBB of these methods, this problem can significantly reduce the query efficiency. In this paper, we propose a novel two-stage adaptive method of clipping the bounding box in spatial query, called IB-CBB (Intersection Based Clipped Bounding Boxes). The first stage employs a clipped bounding box, which records the redundant spatial spaces within the bounding box of the spatial index by calculating the clip points. As a result, the computational complexity of indexed child nodes in the query process is reduced. The second stage optimizes the above query algorithm by judging the intersection of the query box and the MBB of index node, significantly reducing the query time. Experiments demonstrate that IB-CBB outperforms the baseline method in terms of reducing the computational time. [ABSTRACT FROM AUTHOR]

ABSTRACTEfficiently querying multiple spatial datasets is a challenging task in geoscience. The majority of spatial processing techniques use minimum bounding box (MBB) to approximate neighbouring spatial objects and and place them adjacent in the spatial index. However, due to the existence of redundant space in MBB of these methods, this problem can significantly reduce the query efficiency. In this paper, we propose a novel two-stage adaptive method of clipping the bounding box in spatial query, called IB-CBB (Intersection Based Clipped Bounding Boxes). The first stage employs a clipped bounding box, which records the redundant spatial spaces within the bounding box of the spatial index by calculating the clip points. As a result, the computational complexity of indexed child nodes in the query process is reduced. The second stage optimizes the above query algorithm by judging the intersection of the query box and the MBB of index node, significantly reducing the query time. Experiments demonstrate that IB-CBB outperforms the baseline method in terms of reducing the computational time.

Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughput, and real-time performance. The use of streaming processing techniques for spatio-temporal data applications is a promising research direction. However, due to the strong dynamic nature of data in streaming processing systems, traditional spatio-temporal indexing techniques based on relatively static data cannot be used directly in stream-processing environments. It is necessary to study and design new spatio-temporal indexing strategies. Hence, we propose a workload-controllable dynamic spatio-temporal index based on the R-tree. In order to restrict memory usage, we formulate an INSERT and batch-REMOVE (I&BR) method and append a collection mechanism to the traditional R-tree. To improve the updating performance, we propose a time-identified R-tree (TIR). Moreover, we propose a distributed system prototype called a time-identified R-tree farm (TIRF). Experiments show that the TIR could work in a scenario with a controllable usage of memory and a stable response time. The throughput of the TIRF could reach 1 million points per second. The performance of a range search in the TIRF is many times better than in PostgreSQL, which is a widely used database system for spatio-temporal applications. [ABSTRACT FROM AUTHOR]

Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughput, and real-time performance. The use of streaming processing techniques for spatio-temporal data applications is a promising research direction. However, due to the strong dynamic nature of data in streaming processing systems, traditional spatio-temporal indexing techniques based on relatively static data cannot be used directly in stream-processing environments. It is necessary to study and design new spatio-temporal indexing strategies. Hence, we propose a workload-controllable dynamic spatio-temporal index based on the R-tree. In order to restrict memory usage, we formulate an INSERT and batch-REMOVE (I&BR) method and append a collection mechanism to the traditional R-tree. To improve the updating performance, we propose a time-identified R-tree (TIR). Moreover, we propose a distributed system prototype called a time-identified R-tree farm (TIRF). Experiments show that the TIR could work in a scenario with a controllable usage of memory and a stable response time. The throughput of the TIRF could reach 1 million points per second. The performance of a range search in the TIRF is many times better than in PostgreSQL, which is a widely used database system for spatio-temporal applications.