Empirical Systems Engineering and Modeling

The course teaches the core techniques for deriving discrete, well-interpretable qualitative models from observed and measured continuous metrics. Qualitative models reflect “engineering thinking” and help to understand the underlying phenomena and causal relationships in a system, identify bottlenecks, etc. As qualitative models are equipped with precise semantics, formal methods are available to reason about them and to establish proofs of correctness. Computer-based systems are becoming increasingly complex – in the number of their components as well as  their interactions. Intelligent algorithms and highly dynamic IT infrastructures further increase complexity. Therefore, ensuring their extra-functional properties during design and operation is fundamental (e.g., efficiency, performability, and dependability). Thus, modeling current systems for design and operation support requires the design- and runtime use of "system identification" techniques in a classic system theoretic context. The course delivers a theoretical as well as practical overview of identifying qualitative models from observations and measurements; “explaining” models; and reasoning about their correctness. The application of these methods in research as well as in industrial contexts are both covered. The lectures include hands-on practice sessions for each major topic, based on industrially motivated research challenges.

Lectures.

K. D. Forbus: Qualitative Representations. How; People Reason and Learn about the Continuous World. MIT Press 2019.  ; V. Lifschitz: Answer set; programming. Berlin: Springer, 2019.; S. Akama, T. Murai, Y. Kudo: Reasoning with Rough Sets Logical; Approaches to Granularity-Based Framework. Springer 2018.; M. S. Raza, U. Qamar: Understanding and Using Rough Set Based Feature; Selection: Concepts, Techniques and Applications. Springer 2017.; F. Harmelen, V. Lifschitz, and B. Porter, The Handbook of Knowledge; Representation, Elsevier Science San Diego, USA, 2007.; R. Murch, Autonomic Computing. IBM Press, 2004.; A. Földvári, A. Pataricza. "Semi-automated model extraction from; observations for dependability analysis." 2021 IEEE International; Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE,; 2021; I. Kocsis, Á. Salánki, A. Pataricza: „Measurement-Based; Identification of Infrastructures for Trustworthy Cyber-Physical Systems". In:; A. Romanovsky; F. Ishikawa (eds.) Trustworthy Cyber-Physical Systems; Engineering. CRC Press - Taylor and Francis (2016)  ; L. Gönczy, I. Majzik, Sz. Bozóki, A. Pataricza: "MDD-Based Design,; Configuration, and Monitoring of Resilient Cyber-Physical Systems". In; A. Romanovsky; F. Ishikawa (eds.): Trustworthy Cyber-Physical Systems; Engineering. CRC Press - Taylor and Francis (2016) ;  ; A further selection; of papers and web-based sources will be made available to the students during; the course. 

Model-based design, basics of probability theory

Requirements: a. During the semester: One major mid-term homework assignment, preferably related to the particular research topic of the students. Students are encouraged to apply the technology used in the examples presented during the lectures (a collection of Jupyter notebooks) as a “blueprint” for their homework. We waive the examination requirement for outstanding work and propose a term grade based on the homework (which may require solving additional, non-compulsory homework tasks). b. In the examination period: oral examination c. Early exams before the examination period: none Additional possibilities: As per the applicable regulations of the faculty and the university.