上一篇文章中泽西，我提供了一些關于流程挖掘的學習資料。Wil M.P van der Aalst大牛在文章《How to get started with process mining?》也介紹了 如何學習流程挖掘和提高了相關資料残吩。今天我重新閱讀了這篇文章财忽，對著篇文章里面的內容進行整理。

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

在這篇文章中Wil根據(jù)流程挖掘人員對流程挖掘研究程度分為四類泣侮。分別是理解流程挖掘即彪、使用流程挖掘來改進流程、流程挖掘專家或研究人員活尊、流程挖掘開發(fā)者隶校。

《How to get started with process mining?》

階段一：理解流程挖掘

1）流程挖掘書籍：

[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）流程挖掘課程：

Process Mining: Data science in Action

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

階段二：使用流程挖掘來改進流程

1）流程挖掘數(shù)據(jù)集

1.?http://www.processmining.org/

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

3.?https://data.4tu.nl/repository/

原文中提供的數(shù)據(jù)集網站是3TU center，該鏈接已經失效蛹锰，且并沒有找到3TU center深胳。所以提供4TU連接。

2）軟件工具

圖片來至《How to get started with process mining?》?

圖片來至《How to get started with process mining?》?

3）需要思考的問題

當你已經獲得數(shù)據(jù)集宁仔，并且流程工具也已經安裝好后稠屠，通過流程挖掘工具去挖掘數(shù)據(jù)時，你需要在性能分析與合規(guī)性檢查方面思考一下幾個問題翎苫。

1.?人們真正執(zhí)行的流程是哪些权埠？

2.流程中的瓶頸在哪里？

3.人們(或機器)在什么地方偏離了預期的或理想化的流程煎谍？

4.流程中得“高速公路”在哪里攘蔽？

5.影響瓶頸的因素是什么？

6.運行案例時我們能預測問題嗎呐粘？(延遲满俗、偏差转捕、風險等)

7.我們能推薦解決辦法嗎？

8.如何重新設計流程/組織/機器唆垃？

階段三：流程挖掘專家與研究人員

閱讀論文和使用論文中的相關工具五芝，深入理解這些論文。

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.

階段四：流程挖掘開發(fā)者

流程挖掘開發(fā)者應該可以獨立開發(fā)流程工具或在ProM工具基礎上進行構建辕万。

方法

1）查看插件源代碼

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

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

2）設計插件

1.實現(xiàn)自己的思想并與現(xiàn)有技術進行比較枢步。

2.然后通過算法的效率、適應性渐尿、簡單性醉途、泛化性、精確性對算法進行評估砖茸。

3.研發(fā)了解工具本質

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

PS：我不是專業(yè)人員隘擎，本文更多是自己學習記錄，如有不好之處凉夯，歡迎指正货葬。

作者：小聲嘀咕。一個喜歡寫作恍涂、有故事的女同學宝惰。