Process Mining
一、【Process Mining 流程挖掘】
流程挖掘又叫过程挖掘，即从事件日志中提取价值的信息，从而去发现、监控和改进实例流程（即非假定流程）。-- Wil van der Aalst, 过程挖掘:业务过程的发现、合规和改进. 清华大学出版社, 2014.
流程挖掘是业务过程管理（Business Process Management）的扩充和创新。它一方面结合了过程建模和分析的思想，另一方面结合数据挖掘和机器学习的思想。--IEEE流程挖掘工作组
二、入手教程
(1)文献
[1 ]Wil M.P.van der Aalst. Process mining: overview and opportunities[J]. ACM, 2012.
这是一篇系统介绍流程挖掘技术的期刊，通过这篇文章可以对流程挖掘的基本原理以及应用场景有个初步的了解。
[2] Wil M.P.van der Aalst. How to get started with process mining
这篇文章是Wil M.P.van der Aalst给流程挖掘学习人员的一篇关于如何学习流程挖掘的指导性文章。该文章主首先根据流程挖掘学习人员的目前学习进度分为四类，然后根据不同学习进度给出对应流程挖掘学习建议。（强烈建议刚入手学习流程挖掘的学生或研究人员，看这篇！纯干货！）
（2）教材
流程挖掘目前比较著名的教材有以下三本，都是Wil M.P.van der Aalst编写。
[1] Wil M.P.van der Aalst. Process mining: discovery, conformance and enhancement of business processes[M]. Springer Publishing Company, Incorporated, 2011.
这是第一本比较系统的流程挖掘的教材。
[2] Wil M.P.van der Aalst. Process mining: data S science in action[M]. Springer Publishing Company, Incorporated, 2016.
这本书是Wil M.P.van der Aalst在2011年出版书籍的基础上进行改进、修订，出版的第二版。
[3] Wil M.P.van der Aalst. 过程挖掘:业务过程的发现、合规和改进[M]. 王建民, 闻立杰, 译. 清华大学出版社, 2014.
这本书是2011年Wil M.P.van der Aalst书籍的中文版，由清华大学的王建民和闻立杰翻译。
（3）在线课程
目前国外的在线课程主要有以下三个：
1.coursera课程：Process Mining: Data science in Action
2.TU/ek课程：Introduction to Process Mining with ProM
3.TU/e课程：Process Mining in Healthcare
（4）流程挖掘工具ProM
流程挖掘工具有很多，但目前用的最多的就是ProM，很多流程挖掘的研究人员都把自己的研究成果以插件的形式在ProM工具上实现，下面列出ProM工具学习与使用的网站。
通过该网站可以下载ProM工具和事件日志数据，还能了解详细的ProM工具入手教程和使用教程，以及相关实例练习（有时间，可以多在上面看看，有帮助）。
下面列出该网站常用的几个网页：
6.ProM工具论坛
（4）Papers
如果是刚开始研究流程挖掘的研究人员，我建议你先读经典论文。下面给出了流程挖掘领域大牛Wil M.P.van der Aalst论文的两种查阅方式：
1.Wil M.P.van der Aalst在学校上的个人主页
2.Wil M.P.van der Aalst 自己的个人主页
(5)TU/e过程挖掘网站
这个网站有很多过程挖掘的书籍、工具、事件日志的资料。
(6)流程挖掘工作组
这是IEEE建立的一个流程挖掘工作组的网站，这个网站也有很多流程挖掘资料。
三、如何开始学习流程挖掘？
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
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？
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.
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
---文章内容出自于@小声嘀咕
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本文仅是对其他博主关于流程挖掘的相关总结，便于学习总结
