Information on individual educational components (ECTS-Course descriptions) per semester

Advanced Data Management

Degree programme Computer Science - Software and Information Engineering
Subject area Engineering Technology
Type of degree Bachelor
Winter Semester 2024
Course unit title Advanced Data Management
Course unit code 024717050601
Language of instruction English
Type of course unit (compulsory, optional) Elective
Teaching hours per week 3
Year of study 2024
Level of the course / module according to the curriculum
Number of ECTS credits allocated 5
Name of lecturer(s) Peter REITER
Requirements and Prerequisites
  • Knowledge of UML for data modeling.
  • Practical experience with relational databases (SQL).
  • Practical knowledge of at least one programming language.


Course content

This course covers advanced topics in the management of persistent data. These include:

  • Universal patterns for modeling application-specific data structures.
  • Reasons for using standard patterns when designing data models.
  • Advantages and disadvantages of differently abstracted / generalized design patterns.
  • The concept of "time" in relational databases.
    (a) Differences between non-temporal, temporal and bi-temporal data.
    (b) modeling strategies
    (c) "Time" when creating, modifying and querying databases using SQL.
Learning outcomes

"In the context of big data and deep analytics, you have to understand structured and unstructured data in order to be able to extract information from it in a targeted manner. In particular, the design and architecture are key factors in the development of robust data-intensive applications. The generated value is not only created by applications which run on different devices and servers, but through the underlying data. The better you understand this data, the more you benefit from the development, use and analysis of Internet-scale information systems (according to Sridhar Iyengar, "UML and Data Modeling", David C. Hay, 2011) "
The conceptual design is the first step in data modeling and establishes the connection between the "real world" and the technical implementations. Conceptual data models offer the possibility of formally presenting informal technical information and help to understand the data to be stored / processed.

Technical and methodological competence (F / M)

  • Students can name the challenges in the development of conceptual data models and describe common solutions.
  • Students can define and explain the term data dictionary in the context of conceptual / logical data modeling.
  • Students can create data dictionaries for applications.
  • Students can describe and explain different patterns for modeling data structures.
  • Students can explain the advantages and disadvantages of different generalized data models and implement them in SQL.
  • Students can access the created data structures with the help of ORM mappers from an object-oriented programming language.
  • Students can select the appropriate design pattern based on the requirements of an application.
  • Students can name and explain the basic terms of temporal data storage.
  • Students can name the requirements for a database, a data model for storing temporal data and describe standard solutions.
  • The students can make a well-founded decision for an application whether temporal / bi-temporal data storage should be used.
  • Students can implement a temporal database using SQL and formulate time-related queries.

Social and communicative skills (S / K) and self-skills (S)

  • Students can solve tasks independently and on time (reliability).
  • Students can summarize information and present it to the target group (expressiveness and demeanor).
  • The students understand the solutions of others and can make constructive suggestions for improvement and deal with feedback (ability to criticize) and reflect on their own abilities and limits (self-reflection ability).
  • Ability and willingness to acquire new knowledge independently and to learn from successes and failures (learning competence and motivation).
Planned learning activities and teaching methods

Lectures and Exercises

Assessment methods and criteria
  • Assessment of exercises
  • Assessment of a presentation on a selected topic of the course.

For a positive grade, a minimum of 50% of the possible points must be achieved in each part of the examination.


Not applicable

Recommended or required reading

Date, C. J.; Darwen, Hugh; Lorentzos, Nikos (2014): Time and Relational Theory: Temporal Databases in the Relational Model and SQL. 2 edition. Waltham, MA: Morgan Kaufmann. 
Hay, David C. (2011a): Enterprise Model Patterns: Describing the World. Bradley Beach, NJ: Technics Publications, LLC. 
Hay, David C. (2011b): UML and Data Modeling: A Reconciliation. Westfield, N.J: Technics Publications, LLC. 
International, DAMA (2011): The DAMA Dictionary of Data Management, 2nd Edition: Over 2,000 Terms Defined for IT and Business Professionals. 2 edition. Bradley Beach, NJ: Technics Publications, LLC. 
Johnston, Tom (2014): Bitemporal Data: Theory and Practice. Amsterdam ; Boston: Morgan Kaufmann. 
Johnston, Tom; Weis, Randall (2010): Managing Time in Relational Databases: How to Design, Update and Query Temporal Data. Har/Psc. Amsterdam ; Boston: Morgan Kaufmann. 
Silverston, Len (2001a): The Data Model Resource Book, Vol. 1: A Library of Universal Data Models for All Enterprises. Revised edition. New York, NY: Wiley. 
Silverston, Len (2001b): The Data Model Resource Book, Vol. 2: A Library of Data Models for Specific Industries. Revised edition. New York, NY: Wiley. 
Silverston, Len (2009): The Data Model Resource Book, Vol. 3: Universal Patterns for Data Modeling. 1 edition. New York: Wiley.

Mode of delivery (face-to-face, distance learning)

Classroom teaching, attandance is mandatory