Through the optimization of big data transaction process model

the accurate modeling of big data transaction process is realized

which is significant for building a stable

robust and accurate transaction platform. However

the big data transaction process changes over time

and traditional static model optimization methods cannot reflect the characteristics of time-varying changes in real-world process models. For this reason

this paper proposes an optimization approach of big data transaction model. Based on the detection and location of concept drift points

the approach designs a big data transaction log segmentation algorithm and calculates log precise segmentation points to build a large data transaction time-varying segmented model and to realize model optimization. The proposed approach has got used in Tianyuan Big Data Transaction Platform

which shows that the optimization model has an advantage over the static model in fitness

precision and adaptation to the big data transaction process.