Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Empirical Systems Engineering and Modeling

    A tantárgy neve magyarul / Name of the subject in Hungarian: Empirikus modellezés alapú rendszertervezés

    Last updated: 2023. március 6.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Elective PhD course
    Computer Engineering elective
    Electrical Engineering elective
    Business Information Systems elective
    Course ID Semester Assessment Credit Tantárgyfélév
    VIMIDV02   4/0/0/v 5  
    3. Course coordinator and department Dr. Pataricza András,
    4. Instructors Dr. András Pataricza, full professor, Dept. of Measurement and Inf. Systems
    Dr. Imre Kocsis, assistant professor, Dept. of Measurement and Inf. Systems
    5. Required knowledge Model-based design, basics of probability theory
    6. Pre-requisites
    VAGY Training.Code=("5NAM8"))
    ÉS Felevstatusz((Term))="Aktív (Nemzetközi program)" )

    VAGY Training.Code=("5NA374")
    VAGY Training.Code=("5NA384")

    VAGY Training.Code=("5N-M7")
    VAGY Training.Code=("5N-M8")
    VAGY Training.Code=("5N-374")
    VAGY Training.Code=("5N-384")

    VAGY Training.Code=("7NAM03")
    VAGY Training.Code=("7N-M05")
    VAGY Training.Code=("7NAM05")

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    7. Objectives, learning outcomes and obtained knowledge 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.
    8. Synopsis Data collection, model identification, and storage technologies. Fundamental data preparation techniques, profiling, Exploratory Data Analysis (EDA), Confirmatory Data Analysis, and extraction of phenomenological models from observations.
    Engineering thinking, interpretability, and explainability. Basics of hybrid modeling, discretization techniques, and the continuous-qualitative model transition. Qualitative reasoning, statistical validation of essential properties. Mathematical handling of qualitative models. Formal concept analysis. Rough set theory and its applications in modeling for dependability assurance. Modeling from partial information/knowledge. Answer set programming and its application for approximative modeling and diagnosis. Model validation.
    Model representation and reuse of pre-existing knowledge by ontologies, metamodels, knowledge graphs, and graph databases. Consistency checking of observation-derived data and a priori knowledge.
    Model identification case studies: algorithm and software bottleneck identification and tuning; planning performability experiments; software-implemented fault injection; extremity and anomaly analysis; capacity identification; workload engineering for performability assurance; design aspects of “chaos engineering”; integration into MDD-based system design.

    9. Method of instruction Lectures.
    10. Assessment

    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

    11. Recaps As per the applicable regulations of the faculty and the university.
    12. Consultations Appointments shall be made with the lecturers on a case-by-case basis.
    13. References, textbooks and resources

    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. 

    14. Required learning hours and assignment
    Contact hours (lectures)56
    Study during the semester14
    Preparation for midterm exams 
    Preparation of homework36
    Study of written material14
    Preparation for exam30
    15. Syllabus prepared by Dr. András Pataricza    full professor    MIT
    Dr. Imre Kocsis    assistant professor    MIT