CPC G06N 5/022 (2013.01) [G06F 18/214 (2023.01); G06F 18/2323 (2023.01); G06F 18/24147 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01)] | 20 Claims |
1. A data processing method comprising:
obtaining a training sample set that includes a plurality of graph computing task training samples in which data is represented as vertices and associations between the vertices are represented as edges in graph data;
extracting at least one performance indicator feature of each of the graph computing task training samples, the at least one performance indicator feature including one or more of a graph data feature, a graph processing platform feature, a graph algorithm feature, and a machine hardware feature;
generating a target training sample set based on a mapping relationship between actual execution times of the graph computing task training samples and the performance indicator features;
training an initial performance prediction model based on the target training sample set and the mapping relationship to obtain a target performance prediction model; and
outputting, by processing circuitry according to at least one performance indicator feature of an inputted graph computing task test sample, a predicted execution time of the graph computing task test sample based on the target performance prediction model.
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