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作 者:郭艺[1] 叶剑[2,3] 张鹏 GUO Yi;YE Jian;ZHANG Peng(Shandong University of Science and Technology,Qingdao,Shandong 266590,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;The Beijing Key Laboratory of Mobile Computing and Pervasive Device,Beijing 100190,China)
机构地区:[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
出 处:《电子学报》2018年第7期1754-1761,共8页Acta Electronica Sinica
基 金: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;移动计算与新型终端北京市重点实验室研究基金
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.Big data transaction 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 data transaction. Big data transaction is a typical complex process model,which makes the traditional model repair method not able to effectively discover and reduce the deviation between process execution and process rules. This paper proposes an approach of repairing big data transaction 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 model repaired 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. In comparison experiments of those repair methods based on model alignment and the iteration,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.
分 类 号:TP311[自动化与计算机技术—计算机软件与理论]
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