基于偏差约减的大数据交易模型分析与修复方法

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.

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

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.