About this course

The discussions in the course will have a focus on:

  • Database and knowledge representation schemas
  • Introduction to database management systems and their architecture
  • Data and knowledge manipulation in distributed and federated systems
  • SQL and SPARQL as data definition, data manipulation, and data query languages
  • Matematiske og logiske formalismer, t.d. relasjonskalkyle og beskrivingslogikk
  • Mathematical and logical formalisms such as relational calculus and description logic
  • Problems and algorithms for typical categories of queries
  • Formulating and verifying/validating restrictions in SQL, RDFS/OWL, and SHACL, inclduding through model checking
  • Utfordringar ved kunnskapsintegrasjon (uvisse, semantisk heterogenitet, osv.)
  • Challenges at integrating knowledge (uncertainty, semantic heterogeneity, etc.)
  • Data and knowledge bases as components of data spaces and digital twins, e.g. following the paradigm of the data lakehouse
  • Human-computer-interaction for information exchange

Learning outcome

Those who pass INF230 can:

  • Develop data models/schemas tailored to user needs and requirements
  • Use relational databases and formulate SQL queries in an effective way
  • Describe database content and knowledge by means of mathematical-logical formalisms
  • Verifisere konsistens av informasjonsinnhald og samsvar med oppgjevne mønster
  • Verify the consistency of information content and its conformance to specified patterns
  • Develop and set up knowledge-based architectures
  • Kjenne til prinsippa bak datarom, data lakehouses, internet of things mm.
  • Understand the principles behind data spaces, data lakehouses, internet of things, etc.
  • Design and develop user friendly interfaces for information exchange

  • Learning activities

    1. Lectures.
    2. Group work/exercises with teaching assistant present in selected hours.
    3. Compulsory problem-solving.
    4. Independent study.

  • Teaching support

    Canvas, support during tutorial hours, public website (home.bawue.de/~horsch/teaching/), etc.

  • Syllabus

    Will be announced at the beginning of the course.

  • Prerequisites

    INF131 or comparable (basic understanding of logic and data management).

  • Assessment method

    Portfolio evaluation.Grading scale: A-F.

    One written exam Karakterregel: Letter grades Hjelpemiddelkode: A1 No calculator, no other aids Portfolio Karakterregel: Letter grades

  • About use of AI

    K2 - Specified use of generative AI.

    It is not permitted to use generative AI in any way to procude any part of the material undergoing portfolio evaluation, i.e., contributing to the grade for the course. Otherwise generative AI can be used freely.

    Descriptions of AI-category codes.

  • Examiner scheme

    The course coordinator is primarily responsible for preparing the final examination and the compulsory assignments. An internal examiner collaborates with the course coordinator to review the final examination tasks. Grade justification and clarification are provided in accordance with the department's guidelines.

  • Mandatory activity

    In addition to the material for the portfolio evaluation, it is required for each student (or student group) to present at least one tutorial problem solution at the tutorial.

  • Teaching hours

    • Lectures: 2 hours per week for 13 weeks.
    • Exercises: 2 hours per week for 13 weeks.

  • Reduction of credits

    10 credits INF130

  • Admission requirements

    Special requirements in Science.