电子学报 ›› 2018, Vol. 46 ›› Issue (7): 1754-1761.DOI: 10.3969/j.issn.0372-2112.2018.07.031

• 学术论文 • 上一篇    下一篇

基于偏差约减的大数据交易模型分析与修复方法

郭艺1, 叶剑2,3, 张鹏1   

  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:2017-08-24 修回日期:2017-12-24 出版日期:2018-07-25 发布日期:2018-07-25
  • 通讯作者: 张鹏
  • 作者简介:郭艺,男,1993年生于山东滨州,研究生,主要研究方向为过程挖掘、Petri网、大数据.E-mail:13646488081@163.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.2016YFB1001105);国家自然科学基金(No.61401040);工信部2016年集成制造系统集成项目和移动计算与新型终端北京市重点实验室研究基金

Analysis and Repair of Big Data Transaction Model Based on Deviation Reduction

GUO Yi1, YE Jian2,3, ZHANG Peng1   

  1. 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:2017-08-24 Revised:2017-12-24 Online:2018-07-25 Published:2018-07-25

摘要: 大数据交易是促进数据流通和提升数据价值的关键环节.实现大数据交易的过程优化对于构建高效和鲁棒的交易平台至关重要.大数据交易是典型的复杂过程模型,传统的模型修复方法无法有效发现和约减流程执行与流程规则之间存在的偏差.本文提出了一种基于偏差约减的大数据交易模型修复方法,通过过程模型的可达标识图发现事件日志与模型之间的偏差关系,对事件日志与模型之间偏差进行约减,实现基于有效偏差的模型修复.该方法应用于天元大数据网大数据平台,通过与基于模型校准和基于迭代的修复方法进行对比实验,对修复结果开展模型拟合度、精确度、简洁度及时间复杂度评估,验证了方法的有效性.

关键词: 大数据交易, 模型修复, 模型评估, 偏差约减

Abstract: Big datatransaction is a key point of promoting data circulation and data value.It is important for building efficient and robust trading platform to optimize process of big datatransaction.Big data transaction is a typical complex process model,which makes the traditional model repair method not able to effectively discoverand reduce the deviation between process execution and process rules.This paper proposes an approach of repairing big datatransaction model based on deviation reduction.With the help of the reachable marking graph,the approach discovers the deviation between the event log and the process model found,reduces the deviation between the event log and the model,and gets the repaired model based on the effective deviation.At the end of this paper,the proposed approach is used in the Tianyuan big data platform to verify the effectiveness.By comparison experiments of those repair methods based on model alignment and the iteration of the effect of repairing is evaluated from the aspects of fitness,precision,simplicity and time complexity.The evaluation shows that the proposed approach has an advantage over existing methods.

Key words: big data transaction, model repair, model evaluation, deviation reduction

中图分类号: