====== Process Mining ======
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===== News =====
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  * <wrap em>**January 14th, 2019**</wrap>: On Thursday January 17th there will be no lesson
  * <wrap em>**October 25th, 2018**</wrap>: Dear student due to an unforeseen engagement I cannot deliver today's lesson. 
  * October 2nd, 2018: Next lesson will be delivered on October 4th
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===== General Info =====
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**Teachers**: 
  * Andrea Polini, Barbara Re 

**ESSE3 Link**
  * [[https://didattica.unicam.it/Guide/PaginaADContest.do;jsessionid=C355AE2BA49C150FC9B2CCADFF2DCBCA.esse3-unicam-prod-02?ad_cont_id=10025*8952*2018*2016*9999&ANNO_ACCADEMICO=2018&ANNO_COORTE=2018&ANNO_REVISIONE=2018|Process Mining - AY 2018/2019]]
 
**Lessons Scheduling:**
  * 42 h - lecture and exercise sessions  
  * Monday: 2:00 PM – 4:00 PM
  * Thursday: 2:00 PM – 4:00 PM

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===== Course Objectives =====

  * This course focuses on the the missing link between model-based process analysis and data-oriented analysis techniques 
  * The course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains
  * The course explains various process discovery algorithms, 
  * The course introduce conformance techniques to compare processes and event data
  * The course provides to students the opportunity to experiment with real tools

===== Learning Outcomes =====

  * At the end of the course, the students will gain familiarity with process mining terminology, methodology and technologies
  * Be able to apply basic process discovery techniques to learn a process model from an event log  
  * Be able to apply basic conformance checking techniques to compare event logs and process models  
  * Have a good understanding of the data needed to start a process mining project
  * Be able to conduct process mining projects in a structured manner



===== Course Contents =====

  * Introduction to Process Mining and Data Mining. Data. Logs. Play-in, Play Out and Reply. Fundamentals of Process Modelling.
  * From Event Logs to Process Models. Getting Data. Process Discovery. Discovery Techniques an Introduction.  Tools support an introduction: Apromore, Disco, and ProM.
  * Discovery Algorithms. Alfa,  Heuristic, Genetic Miner. 			
  * Conformance Checking. Business Alignment and Auditing. Token Replay. Process Drift. Business Process Model and Instances. Business Process Life-Cycle. Classification of Business Process.
 

===== Study material =====
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** Classes Scheduling **
  * {{ :didattica:magistrale:pm:ay_1819:scheduling_process_mining_-_foglio1.pdf |tentative}}

** Course Slides **
  * {{ :didattica:magistrale:pm:ay_1819:slide_1_-_course_presentation.pdf | Course Introduction}}
  * {{ :didattica:magistrale:pm:ay_1819:01_context.pdf |Process Mining and its Context}}
  * {{ :didattica:magistrale:pm:ay_1819:02_modeling.pdf |Formal Modeling of Processes}} 
  * {{ :didattica:magistrale:pm:ay_1819:03_datamining.pdf |Data Mining - a short introduction}}
  * {{ :didattica:magistrale:pm:ay_1819:gettingdata.pdf |Getting Data}}
  * {{ :didattica:magistrale:pm:ay_1819:processdiscoveryanintroduction.pdf |Process Discovery an Introduction}}
  * {{ :didattica:magistrale:pm:ay_1819:processdiscoveryanintroduction.pdf |Alpha Algorithm: A Process Discovery Algorithm}}
  * {{ :didattica:magistrale:pm:ay_1819:processdiscoveryintroduction.pdf |Alpha Algorithm: Limitation}}
  *  {{ :didattica:magistrale:pm:ay_1819:advancedprocessdiscoverytechniques.pdf |Quality, Heuristic and Genetic mining}}
  * {{ :didattica:magistrale:pm:ay_1819:simonelli.pdf |Mining Experience}} ({{ :didattica:magistrale:pm:ay_1819:final_version_published.pdf |comparison}}) ({{ :didattica:magistrale:pm:ay_1819:bustech_2017_1_30_90020.pdf |methodology}})
  * {{ :didattica:magistrale:pm:ay_1819:04_conformance.pdf |Conformance Checking}}
  * {{ :didattica:magistrale:pm:ay_1819:05_additional.pdf |Mining Additional Perspectives}}
 





** Streaming **
  * October 8th, 2019
  * [[https://unicam.webex.com/unicam/ldr.php?RCID=fe2bfba5c2586a1fb53df3f6df3967d3|January 7th, 2019]]
  * [[https://unicam.webex.com/unicam/ldr.php?RCID=6b05f179855dc44ac9c7db863fcfc483|January 14th, 2019]]
  * [[https://unicam.webex.com/unicam/ldr.php?RCID=04a7a61b1160c7f5e88c3c73e4573688|January 21st, 2019]]

**Reference book:**
  * Process Mining: Data Science in Action by W.M.P. van der Aalst, Springer Verlag, 2016 (ISBN 978-3-662-49850-7). From chapter 1 to 9. 

**Tools:**
  * Apromore   - http://apromore.unicam.it/ 
  * Disco -  https://fluxicon.com/disco/
  * ProM - http://www.promtools.org/


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===== Exams =====
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**Project Based on 2018 Business Process Intelligence Challenge (BPIC) - [[https://www.win.tue.nl/bpi/doku.php?id=2018:challenge]] ** 

**Project Based Examination** on the topics of the syllabus including project presentation

  - Process Mining Challenge: (i) we will provide real data set, (ii) we will provide questions to be answered
  - We expect that you can focus on a specific aspect of interest and analyze this aspect in great detail using any technique, method, algorithm, and tool
  - We will judge based on: (i) the originality of the results, (ii) the validity of the claims, and (iii) the depth of the analysis of specific issues identified


**Dates** (https://didattica.unicam.it/Home.do)




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