First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
DATA SCIENCE
Course unit
PROCESS MINING
SCP7079235, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
DATA SCIENCE
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination PROCESS MINING
Website of the academic structure http://datascience.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge CHIARA GHIDINI
Other lecturers CHIARA DI FRANCESCOMARINO

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
SCP7079235 PROCESS MINING CHIARA GHIDINI SC1176

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses INF/01 Computer Science 3.0
Core courses ING-INF/05 Data Processing Systems 3.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: Basic notions of algorithms, data structures and programming.
Target skills and knowledge: The aim of the course is to introduce and investigate the main methods and concepts that pertain the modeling and analysis of business processes. More in detail, the course will focus on the main modeling languages (BPMN, Petri Nets, and Declare), on the main methodologies for manual modeling and analysis and on the main algorithms for the (semi)automatic modeling and analysis (the so-called process mining). By exploiting concrete software platforms several algorithms will be also investigated in a 'hands-on' fashion on real data.
At the end of the course the students should have a detailed knowledge of the main methods and concepts of business process modeling e mining, of the main metrics used to support the analysis of business processes and of the main algorithms of process mining.
Examination methods: Written exam and project. The project is due and has to be discussed by the end of the course.
Assessment criteria: The project work, and the written exam, will be evaluated on the basis of the following criteria: i) student’s knowledge of the concepts, methods, and technologies; ii) ability of the student to master the implementation technology; iii) student’s capacity for synthesis, clarity, and abstraction, as demonstrated by the written exam and project presentation. The final grade is obtained as the weighted sum of the grades of the written exam (80%) and the project (20%)
Course unit contents: The course will cover the topics listed below:

1. MODELING AND ANALISYS: THE BPMN PERSPECTIVE
- Process Identification
- Essential and Advanced Process Modeling in BPMN
- Qualitative Analysis
- Quantitative Analysis
- Process redesign

2. MODELING AND ANALISYS: THE PETRI NET PERSPECTIVE
- An introduction to Petri Nets
- Petri nets and colored petri nets
- Simulation based analysis
- Reachability and coverability analysis
- Process modeling and analysis with PN

3. PROCESS MINING
- Data & Process mining
- Getting the data: the construction of event logs
- An introduction to Process discovery
- Advanced process discovery
- Conformance checking - replay based
- Conformance checking - logic based
- Mining additional perspectives
- Typical use cases, e.g., medical processes

4. DECLARATIVE APPROACHES
- Declarative approaches and Declare
- Declarative process mining (discovery in Declare) and hybrid approaches

5. PREDICTIVE PROCESS MONITORING
- Basic Predictive Process Monitoring techniques
- Advanced Predictive Process Monitoring techniques
Planned learning activities and teaching methods: The course consists of lectures. Some practical activities and exercises will require the use of computers.
Additional notes about suggested reading: Slides, exercises and scientific papers will be provided.
Textbooks (and optional supplementary readings)
  • Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. Reijers., Fundamentals of Business Process Management,. --: Springer, 2013. for part 1. MODELING AND ANALISYS: THE BPMN PERSPECTIVE Cerca nel catalogo
  • Wil M.P. van der Aalst and Christian Stahl, Modeling Business Processes: A Petri Net-Oriented Approach. --: Information Systems, 2011. for part 2. MODELING AND ANALISYS: THE PETRI NET PERSPECTIVE Cerca nel catalogo
  • W. van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin: Springer-Verlag, 2011. for part 3. PROCESS MINING Cerca nel catalogo