上一篇文章中，我提供了一些关于流程挖掘的学习资料。Wil M.P van der Aalst大牛在文章《How to get started with process mining?》也介绍了 如何学习流程挖掘和提高了相关资料。今天我重新阅读了这篇文章，对着篇文章里面的内容进行整理。

Wil M.P van der Aalst 《How to get started with process mining?》  

在这篇文章中Wil根据流程挖掘人员对流程挖掘研究程度分为四类。分别是理解流程挖掘、使用流程挖掘来改进流程、流程挖掘专家或研究人员、流程挖掘开发者。

《How to get started with process mining?》

阶段一：理解流程挖掘

1）流程挖掘书籍：

[Wil M.P.van der Aalst. Process mining: discovery, conformance and enhancement of business processes[M]. Springer Publishing Company, Incorporated, 2011.

https://www.springer.com/gp/book/9783642193453#otherversion=9783642193446

2）流程挖掘课程：

Process Mining: Data science in Action

https://www.coursera.org/learn/process-mining

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阶段二：使用流程挖掘来改进流程

1）流程挖掘数据集

1. http://www.processmining.org/

2. https://www.win.tue.nl/ieeetfpm/doku.php

3. https://data.4tu.nl/repository/

原文中提供的数据集网站是3TU center，该链接已经失效，且并没有找到3TU center。所以提供4TU连接。

2）软件工具

图片来至《How to get started with process mining?》  图片来至《How to get started with process mining?》 

3）需要思考的问题

当你已经获得数据集，并且流程工具也已经安装好后，通过流程挖掘工具去挖掘数据时，你需要在性能分析与合规性检查方面思考一下几个问题。

1. 人们真正执行的流程是哪些？

2.流程中的瓶颈在哪里？

3.人们(或机器)在什么地方偏离了预期的或理想化的流程？

4.流程中得“高速公路”在哪里？

5.影响瓶颈的因素是什么？

6.运行案例时我们能预测问题吗？(延迟、偏差、风险等)

7.我们能推荐解决办法吗？

8.如何重新设计流程/组织/机器？

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阶段三：流程挖掘专家与研究人员

阅读论文和使用论文中的相关工具，深入理解这些论文。

1.W.M.P. van der Aalst, A. Adriansyah, and B. van Dongen.Replaying History on Process Models forConformance Checking and PerformanceAnalysis.  WIREs Data Mining and Knowledge Discovery, 2(2):182-192, 2012.

2.W.M.P. van der Aalst. Business Process Management: A Comprehensive Survey. ISRN Software Engineering, pages 1-37, 2013. doi:10.1155/2013/507984.

3.W.M.P. van der Aalst, K.M. van Hee, A.H.M. ter Hofstede, N. Sidorova, H.M.W. Verbeek, M. Voorhoeve, and M.T. Wynn. Soundness of Workflow Nets: Classification, Decidability, andAnalysis. Formal Aspects of Computing, 23(3):333-363, 2011.

4.W.M.P. van der Aalst. Decomposing Petri Nets for Process Mining: A Generic Approach.Distributed and Parallel Databases, 31(4):471-507, 2013.

5.W.M.P. van der Aalst. Business Process Simulation Survival Guide. In J. vom Brocke and M. Rosemann, editors, Handbook on Business Process Management 1, International Handbooks on Information Systems, pages 337-370. Springer-Verlag, Berlin, 2015.

6. W.M.P. van der Aalst. Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining. In M. Song, M. Wynn, and J. Liu, editors, Asia Pacific Conference on  Business Process Management (AP-BPM 2013), volume 159 of Lecture Notes in Business Information Processing, pages 1-22. Springer-Verlag, Berlin, 2013.

7. W.M.P. van der Aalst. Extracting Event Data from Databases to Unleash Process Mining. In J. Vom Brocke and T. Schmiedel, editors, Business Process Management Roundtable 2014, Springer-Verlag, Berlin, 2015.           

8. M. de Leoni, W.M.P. van der Aalst, and M. Dees. A General Framework for Correlating  Business Process Characteristics. In S. Sadiq, P. Soffer, and H. Voelzer,editors, International Conference on Business Process Management (BPM 2014), volume 8659 of Lecture Notes in Computer Science, pages 250-266. Springer-Verlag, Berlin, 2014.

9.S.J.J. Leemans, D. Fahland, and W.M.P. van der Aalst. Process and Deviation Exploration with Inductive Visual Miner. In L. Limonad and B. Weber, editors, Business Process Management Demo Sessions (BPMD 2014), volume 1295 of CEUR Workshop Proceedings, pages 46-50. CEUR-WS.org, 2014.

10.W.M.P. van der Aalst. Process Mining in the Large: A Tutorial. In E. Zimanyi,editor, Business Intelligence (eBISS 2013), volume 172 ofLecture Notes in Business Information Processing, pages 33-76. Springer-Verlag, Berlin, 2014.

11.R.P. Jagadeesh Chandra Bose, W.M.P. van der Aalst, I. Zliobaite, and M. Pechenizkiy. Dealing  With Concept Drifts in Process Mining. IEEE Transactions on Neural Networks and Learning Systems, 25(1):154-171, 2014.

12.S.J.J. Leemans, D. Fahland, and W.M.P. van der Aalst. Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour. In N. Lohmann, M. Song, and P. Wohed, editors,Business Process Management Workshops, International Workshop on Business Process Intelligence (BPI 2013), volume 171 of Lecture Notes in Business Information Processing, pages 66-78. Springer-Verlag, Berlin, 2014.

13.R.P. Jagadeesh Chandra Bose, R. Mans, and W.M.P. van der Aalst. Wanna Improve Process Mining Results? It's High Time We Consider Data Quality Issues Seriously. In B. Hammer, Z.H. Zhou, L. Wang, and N. Chawla, editors, IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013), pages 127-134, Singapore, 2013. IEEE.

14.R.P. Jagadeesh Chandra Bose and W.M.P. van der Aalst. Discovering Signature Patterns from Event Logs. In B. Hammer, Z.H. Zhou, L. Wang, and N. Chawla, editors, IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013), pages 111-118, Singapore, 2013. IEEE.

15.W.M.P. van der Aalst. A General Divide and Conquer Approach for Process Mining.In M. Ganzha, L. Maciaszek, and M. Paprzycki, editors,Federated Conference on Computer Science and Information Systems (FedCSIS 2013), pages 1-10. IEEE Computer Society, 2013.

16.M. De Leoni and W.M.P. van der Aalst. Data-Aware Process Mining: Discovering Decisions in Processes Using Alignmentson. In S.Y. Shin and J.C. Maldonado, editors, ACM Symposium  Applied Computing (SAC 2013), pages 1454-1461. ACM Press, 2013.

17.S.J.J. Leemans, D. Fahland, and W.M.P. van der Aalst. Discovering Block-structured Process Models from Event Logs: A Constructive Approach. editors. In J.M. Colom and J. Desel, Applications and Theory of Petri Nets 2013, volume 7927 of Lecture Notes in Computer Science, pages 311-329. Springer-Verlag, Berlin, 2013.

18.D. Fahland and W.M.P. van der Aalst. Simplifying Discovered Process Models in a . Controlled Manner. Information Systems, 38(4):585-605, 2013.

19.A. Rozinat, M. Wynn, W.M.P. van der Aalst, A.H.M. ter Hofstede, and C. Fidge. Workflow Simulation for Operational Decision Support. Data and Knowledge Engineering, 68(9):834-850, 2009.

20.A. Rozinat, R.S. Mans, M. Song, and W.M.P. van der Aalst. Discovering Simulation Model. Information Systems, 34(3):305-327, 2009.

21.A. Rozinat and W.M.P. van der Aalst.Conformance Checking of Processes Based onMonitoring Real Behavior. Information Systems, 33(1):64-95, 2008.

22.W.M.P. van der Aalst, H.A. Reijers, and M. Song. Discovering Social Networks from Event Logs. Computer Supported Cooperative work, 14(6):549-593, 2005.

23.C.W. Günther and W.M.P. van der Aalst. Fuzzy Mining: Adaptive Process Simplification Based on Multi-perspective Metricseditors. In G. Alonso, P. Dadam, and M. Rosemann, editors, International Conference on Business Process Management (BPM 2007), volume 4714 of Lecture Notes in Computer Science, pages 328-343. Springer-Verlag, Berlin, 2007.

24.IEEE Task Force on Process Mining. Process Mining Manifesto. In F. Daniel, K. Barkaoui, and S. Dustdar, editors, Business Process Management Workshops, volume 99 of Lecture Notes in Business Information Processing, pages 169-194. Springer-Verlag, Berlin, 2012.

25.W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Discovering Process Models from Event Logs.IEEE Transactions on Knowledge and Data Engineering, 16(9):1128-1142, 2004.

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阶段四：流程挖掘开发者

流程挖掘开发者应该可以独立开发流程工具或在ProM工具基础上进行构建。

方法

1）查看插件源代码

论坛： https://svn.win.tue.nl/trac/prom/wiki/Contribute

邮箱列表： https://svn.win.tue.nl/trac/prom/wiki/MailingLists

2）设计插件

1.实现自己的思想并与现有技术进行比较。

2.然后通过算法的效率、适应性、简单性、泛化性、精确性对算法进行评估。

3.研发了解工具本质

ProM, Disco, Celonis Process Mining, Minit, myInvenio, Perceptive Process Mining, QPR ProcessAnalyzer

PS：我不是专业人员，本文更多是自己学习记录，如有不好之处，欢迎指正。

作者：小声嘀咕。一个喜欢写作、有故事的女同学。