Abstract: A spatial join query method and apparatus, an electronic device, and a storage medium. The spatial join query method comprises: obtaining a first resilient distributed dataset of first spatial data and a second resilient distributed dataset of second spatial data; generating multi-tree spatial indexes for spatial partitions, and collecting statistics about spatial distribution information of geometric objects according to the multi-tree spatial indexes to obtain global spatial distribution information; determining an overlapping spatial partition according to the global spatial distribution information; and setting data, in the first spatial data and the second spatial data, corresponding to the overlapping spatial partition as target data, and performing spatial join calculation on the target data.

Abstract: The present disclosure relates to the technical field of computers, and provides a nearest neighbor trajectory query method and apparatus, an electronic device, and a readable storage medium. The nearest neighbor trajectory query method comprises: acquiring a first trajectory to be queried, and determining spatial position information of the first trajectory; generating a trajectory signature according to a positional relationship between the spatial position information of the first trajectory and a region to be queried; determining a spatial position index code of the first trajectory by means of XZ-Ordering; and constructing a spatial range index of the first trajectory according to the trajectory signature and the spatial position index code.

Abstract: Disclosed are a method and an apparatus for model training and data enhancement, an electronic device and a storage medium. A generative adversarial network model includes a generator and two discriminators, an output of the generator is used as an input of the two discriminators, the method including: generating, by the generator, reference sample data; calculating, by the first discriminator, a first distance between the reference sample data and preset negative sample data; calculating, by the second discriminator, a second distance between negative class data composed of the reference sample data and the preset negative sample data and preset positive sample data; determining an objective function based on the first distance and the second distance; and training the generative adversarial network model by using the objective function until the generative adversarial network model converges, to obtain the generative adversarial network model.

Abstract: A method and a device for improving performance of a data processing model, a storage medium and an electronic device are provided. A piece of data in a determined test data read currently is determined as target data. Outlier detection parameters in a detection module are acquired. Detection of concept drift is performed on the data processing model based on the target data and the outlier detection parameters. A detection module is triggered to update each of the outlier detection parameters and the data processing model is retrained when concept drift is successfully detected. After the data processing model is already trained, a piece of data to be read next is determined as the target data, the updated outlier detection parameters in the detection module are acquired, and the detection is resumed until all the pieces of data in the test data stream are read.