Multi-dimensional query method of satellite remote sensing data on heterogeneous computing platform

Abstract

The invention discloses a multi-dimensional query method of satellite remote sensing data on a heterogeneous computing platform. The method comprises the following steps: carrying out data distribution on a remote sensing satellite multi-dimensional data set; optimizing a multi-dimensional index structure, standardizing original main keys, replacing the original main keys with continuous numericalvalue main keys, shielding semantic information of the main keys in the original dimensional data, updating foreign keys on an original fact table, and eliminating semantic information of multi-dimensional indexes of the fact data; carrying out distributed storage on the OLAP multi-dimensional data set oriented to the heterogeneous computing platform; discomposing an OLAP multi-dimensional data set query processing task for remote sensing data into three sub-tasks on three different computing platforms. By means of the method, the requirement for GPU memory capacity is reduced; the design issimple; the collaborative computing of the heterogeneous computing platform is realized, the hardware processing capability is fully utilized, the overall hardware cost is reduced, and the comprehensive OLAP query processing performance is improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing satellites, in particular to a multi-dimensional query method for satellite remote sensing data on a heterogeneous computing platform. Background technique [0002] Satellite remote sensing data has multidimensional attributes, corresponding to remote sensing observation values ​​in multiple dimensions such as time, orbit number, payload, channel, and position, and constitutes a typical multidimensional data set. In the process of data processing and data service, it is necessary to complete multi-dimensional query for remote sensing datasets to obtain relevant data subsets. Satellite remote sensing data corresponds to a large-scale data cube. The OLAP query process requires a large amount of storage and computing resources, and consumes a long computing time. [0003] Traditionally, database operations rely on general-purpose CPUs to perform OLAP query processing tasks. The number of CPU ...

AI Technical Summary

Problems solved by technology

Satellite remote sensing data corresponds to a large-scale data cube. The OLAP query process requires a large amount of storage and computing resources, and consumes a long computing time.

[0003] Traditionally, database operations rely on general-purpose CPUs to perform OLAP query processing tasks. The number of CPU cores and main frequency performance restrict the real-time OLAP query processing performance of large data volumes.

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