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Regular version of the site
Master 2021/2022

Research Seminar "Software Engineering: Development Management"-2

Type: Compulsory course (System and Software Engineering)
Area of studies: Software Engineering
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Master’s programme: Software and Systems Engineering
Language: English
ECTS credits: 7

Course Syllabus

Abstract

Modern information systems accumulate significant amounts of event data, which include, for instance, transaction logs, message logs and different records of user activity. These data are commonly referred to as event logs consisting of ordered event sequences called traces. Event logs use in process mining to discover models of real processes. The expected behavior of an information system is usually specified at the beginning stages of the life cycle. Discovering the real behavior of processes from event logs is an important problem, since manually created models do not reflect changes made during the operation period of an information systems. A wide range of algorithms for the automated discovery of process models have been proposed over recent years. The behavior of processes can be represented in various notations, including different classes of Petri nets, heuristic and causal nets, or Business Process Model and Notation (BPMN). The quality of process discovery algorithms is determined by the quality of discovered process models. Conformance checking is another important part of process mining. Conformance checking offers quality dimensions, which measure the correspondents between an event log (observed behavior) and a process model as well as the complexity of discovered models. Data acquired during conformance checking form the basis for the further enhancement and the reliability improvement of processes, occurring in information systems. During this research seminar, students will familiarize themselves with the fundamental principles and key application areas of process discovery, conformance checking and process enhancement by presenting and reviewing reports based on conference and journal papers.
Learning Objectives

Learning Objectives

  • To learn the fundamental principles of process mining and its main components: process discovery, conformance checking and enhancement
  • To study process mining algorithms and approaches
  • To gain practical experience in the critical discussion of scientific papers
Expected Learning Outcomes

Expected Learning Outcomes

  • Students will know the basic principles of process discovery, conformance checking and process mining constituting the area process mining.
  • Students will learn the most wide-spread algorithms, which support the automated process discovery.
  • Students will learn the most wide-spread algorithms, which support the automated check of the conformance between a discovered process model and an event log.
  • Students will gain the experience of making reports covering the main contributions of research articles.
  • Students will gain the experience of reviewing and analyzing reports made by others.
Course Contents

Course Contents

  • Process Mining: Essentials
    Event Logs: traces, actions, attributes, timestamps. Discovering process models from event logs: algorithms, approaches. Conformance checking between discovered models and event logs: fitness, precision, and generalization. Enhancement of process models: repair of process models.
  • Process Mining in Software Engineering
    Analyzing (stack) traces of software: architecture, bottlenecks, identification of subprograms, interactions between components of a program. Analyzing patterns and anti-patterns in program behavior: cancellations, exceptional behaviors. Using process mining is specific application areas through the lens of software engineering: healthcare, education, banking, insurance etc.
Assessment Elements

Assessment Elements

  • non-blocking O_report
  • non-blocking O_review
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    The final mark for the research seminar Ofinal is evaluated by the following formula: Ofinal = 0,6 * Oreport + 0,4 * Oreport + Oextra - Oskipped, where Oextra assesses the amount of extra work as a speaker/reviewer; additional experiments with software tools; analysis of the practical application, further works on the topic; active participation in the discussion (especially, without being a «compulsory» reviewer). Oskipped depends on the number of skipped classes (at most, 2 classes can be skipped without influencing the final mark): Oskipped = (# of skipped classes - 2) / 4, i.e., i.e., every skipped class (more than two) will subtract 0.25 from the final mark for the research seminar.
Bibliography

Bibliography

Recommended Core Bibliography

  • Aalst, W. van der. (2016). Process Mining : Data Science in Action (Vol. Second edition). Heidelberg: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1203872
  • Josep Carmona, Boudewijn van Dongen, Andreas Solti, & Matthias Weidlich. (2018). Conformance Checking : Relating Processes and Models (Vol. 1st ed. 2018). Springer.

Recommended Additional Bibliography

  • Eric Badouel, Luca Bernardinello, & Philippe Darondeau. (2015). Petri Net Synthesis (Vol. 1st ed. 2015). Springer.
  • Wolfgang Reisig. (2013). Understanding Petri Nets : Modeling Techniques, Analysis Methods, Case Studies (Vol. 2013). Springer.