基于概念漂移检测的大数据交易过程模型优化方法

doi:10.3969/j.issn.0372-2112.2019.07.009

电子学报 ›› 2019, Vol. 47 ›› Issue (7): 1465-1474.DOI: 10.3969/j.issn.0372-2112.2019.07.009

基于概念漂移检测的大数据交易过程模型优化方法

张鹏¹, 叶剑^2,3, 张鹏¹

1. 山东科技大学, 山东青岛 266590;
2. 中国科学院计算技术研究所, 北京 100190;
3. 移动计算与新型终端北京市重点实验室, 北京 100190

收稿日期:2018-04-26 修回日期:2018-12-24 出版日期:2019-07-25
- 通讯作者:
- 张鹏
- 作者简介:
- 张鹏男,1994年生于安徽阜阳.研究生.主要研究方向为过程挖掘、Petri网、大数据.E-mail:1192360811@qq.com;叶剑男,1974年生于山东济南.博士,高级工程师,硕士研究生导师.主要研究方向为移动互联网挖掘、普适计算.E-mail:jye@ict.ac.cn
- 基金资助:
- 国家重点研发计划 (No.2016YFB1001100）; 工信部2016年绿色制造系统集成项目; 工信部2018年工业互联网创新发展项目和移动计算与新型终端北京市重点实验室研究基金

Optimization of Big Data Transaction Process Model Based on Concept Drift Detection

ZHANG Peng¹, YE Jian^2,3, ZHANG Peng¹

1. Shandong University of Science and Technology, Qingdao, Shandong 266590, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. The Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China

Received:2018-04-26 Revised:2018-12-24 Online:2019-07-25 Published:2019-07-25

摘要/Abstract

摘要： 通过大数据交易过程模型优化，实现对大数据交易过程的精确建模，对于构建稳定、鲁棒和精确的交易平台至关重要.然而，大数据交易流程随时间而变化，传统的静态模型优化方法无法反映现实流程模型的时态变化特征.为此，本文提出一种基于概念漂移的大数据交易模型优化方法，在概念漂移点检测和定位的基础上，设计大数据交易日志分割算法，演算日志精准分割点，构建具有时变特性的大数据交易分段模型，实现基于日志分割的模型优化.该方法在天元大数据交易平台的应用实践表明，优化模型在拟合度和精确度方面均优于静态模型，对大数据交易演化过程的适配性更强.

关键词: 大数据交易, 概念漂移, 日志分割, 模型评估

Abstract: 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.

Key words: big data transaction, concept drift, log segmentation, model evaluation

中图分类号:

TP311

张鹏, 叶剑, 张鹏. 基于概念漂移检测的大数据交易过程模型优化方法[J]. 电子学报, 2019, 47(7): 1465-1474.

ZHANG Peng, YE Jian, ZHANG Peng. Optimization of Big Data Transaction Process Model Based on Concept Drift Detection[J]. Acta Electronica Sinica, 2019, 47(7): 1465-1474.

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基于概念漂移检测的大数据交易过程模型优化方法

Optimization of Big Data Transaction Process Model Based on Concept Drift Detection

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