基于概念漂移检测的大数据交易过程模型优化方法
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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.
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.
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.
引文
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[15] BOSE R P J C,AALST W M P V D,?LIOBAIT I,et al.Handling concept drift in process mining[J].Lecture Notes in Computer Science,2011,4(1):391-405.
[16] BAHNINI A,PLISSONNIER D,KOSKAS F,et al.Online discovery of declarative process models from event streams[J].IEEE Transactions on Services Computing,2015,8(6):833-846.
[17] MAARADJI A,DUMAS M,ROSA M L,et al.Detecting sudden and gradual drifts in business processes from execution traces[J].IEEE Transactions on Knowledge & Data Engineering,2017,29(10):2140-2154.
[18] BOSE R P,WM V D A,ZLIOBAITE I,et al.Dealing with concept drifts in process mining[J].IEEE Transactions on Neural Networks & Learning Systems,2014,25(1):154.
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[20] 天元大数据交易平台[DB/OL].https://www.tdata.cn/.2018-10-01.
[21] ROZINAT A,AALST W M P V D.Conformance checking of processes based on monitoring real behavior[J].Information Systems,2008,33(1):64-95.
[22] MANNHARDT F,LEONI M,REIJERS H A,et al.Balanced multi-perspective checking of process conformance[J].Computing,2016,98(4):407-437.
[23] MUNOZ-GAMA J,CARMONA J,AALST W M P V D.Conformance checking in the large:partitioning and topology[A].International Conference on Business Process Management[C].Berlin:Springer-Verlag,2013.130-145.
[24] AALST W V D,ADRIANSYAH A,DONGEN B V.Replaying history on process models for conformance checking and performance analysis[J].Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery,2012,2(2):182-192.
[2] 宋梅青.融合数据分析服务的大数据交易平台研究[J].图书情报知识,2017(2):13-19.
[3] 郭艺,叶剑,张鹏.基于偏差约减的大数据交易模型分析与修复方法[J].电子学报,2018,46(7):1754-1761.GUO Yi,YE Jian,ZHANG Peng.Analysis and repair of big data transaction model based on deviation reduction[J].Acta Electronica Sinica,2018,46(7):1754-1761.(in Chinese)
[4] BAKLIZKY M,FANTINATO M,THOM L H,et al.Business process point analysis:survey experiments[J].Business Process Management Journal,2017,23(2):399-424.
[5] AALST W M P V D,MEDEIROS A K A D,WEIJTERS A J M M.Genetic process mining[J].Lecture Notes in Computer Science,2005,14(2):76-83.
[6] Van der AALST Will.Process Mining:Data Science in Action[M].Berlin:Springer,2016.
[7] 杜玉越,朱鸿儒,王路,等.一种基于逻辑Petri网的过程挖掘方法[J].电子学报,2016,44(11):2742-2751.DU Yu-yue,ZHU Hong-ru,WANG Lu,et al.A method of process mining based on logic petri nets[J].Acta Electronica Sinica,2016,44(11):2742-2751.(in Chinese)
[8] KALENKOVA A A,van der AALST W,LOMAZOVA I A,et al.Process mining using BPMN:relating event logs and process models[A].Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems[C].US:ACM,2016.123-123.
[9] ZHANG X,DU Y,QI L,et al.Repairing process models containing choice structures via logic petri nets[J].IEEE Access,2018,PP(99):DOI:10.1109/ACCESS.2018.2870727.
[10] Van der AALST W.Decomposing petri nets for process mining:A generic approach[J].Distributed & Parallel Databases,2013,31(4):471-507.
[11] WYNN M T,et al.Impact-driven process model repair[J].ACM Transactions on Software Engineering & Methodology,2016,25(4):28-80.
[12] FAHLAND D,AALST W.Model repair — aligning process models to reality[J].Information Systems,2015,47(1):220-243.
[13] AALST W,ADRIANSYAH A,MEDEIROS A K A D,et al.Process mining manifesto[A].International Conference on Business Process Management[C].Berlin,Heidelberg:Springer,2011.169-194.
[14] 孙雪,李昆仑,韩蕾,等.基于特征项分布的信息熵及特征动态加权概念漂移检测模型[J].电子学报,2015,43(7):1356-1361.SUN Xue,LI Kun-lun,HAN Lei,et al.Construction of the concept drift detection model based on the information entropy of feature distribution and dynamic weighting algorithm[J].Acta Electronica Sinica,2015,43(7):1356-1361.(in Chinese)
[15] BOSE R P J C,AALST W M P V D,?LIOBAIT I,et al.Handling concept drift in process mining[J].Lecture Notes in Computer Science,2011,4(1):391-405.
[16] BAHNINI A,PLISSONNIER D,KOSKAS F,et al.Online discovery of declarative process models from event streams[J].IEEE Transactions on Services Computing,2015,8(6):833-846.
[17] MAARADJI A,DUMAS M,ROSA M L,et al.Detecting sudden and gradual drifts in business processes from execution traces[J].IEEE Transactions on Knowledge & Data Engineering,2017,29(10):2140-2154.
[18] BOSE R P,WM V D A,ZLIOBAITE I,et al.Dealing with concept drifts in process mining[J].IEEE Transactions on Neural Networks & Learning Systems,2014,25(1):154.
[19] MANOJ Kumar M V,LIKEWIN Thomas,ANNAPPA B.Capturing the sudden concept drift in process mining[A].Algorithms & Theories for the Analysis of Event Data (ATAED’15)[C].Brussels,Belgium,2015.132.
[20] 天元大数据交易平台[DB/OL].https://www.tdata.cn/.2018-10-01.
[21] ROZINAT A,AALST W M P V D.Conformance checking of processes based on monitoring real behavior[J].Information Systems,2008,33(1):64-95.
[22] MANNHARDT F,LEONI M,REIJERS H A,et al.Balanced multi-perspective checking of process conformance[J].Computing,2016,98(4):407-437.
[23] MUNOZ-GAMA J,CARMONA J,AALST W M P V D.Conformance checking in the large:partitioning and topology[A].International Conference on Business Process Management[C].Berlin:Springer-Verlag,2013.130-145.
[24] AALST W V D,ADRIANSYAH A,DONGEN B V.Replaying history on process models for conformance checking and performance analysis[J].Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery,2012,2(2):182-192.
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