Advanced Data Management (E)

• Practical experience with relational databases (SQL).
• Practical knowledge of at least one programming language.
• Experience in working with container tools (Docker, Podman)

This course deals with advanced topics of persistent data management. The focus is specifically on IIoT time series data produced by autonomous assets (machines, robots). This includes, among other things:

• The Unified Namespace concept for managing store floor events
• The use of modern data technologies such as Redis, Kafka and time series databases
• Understanding common protocols in the Industry 4.0 environment (MQTT, OPC-UA, MODBUS)
• The concept of Continuous Aggregates to aggregate the flood of data without losing valuable information
• The concepts of "Data Mesh" and "Data Product" to quickly and easily provide the user with all relevant data in the right format
• Concepts for effective visualization of time series data

The ongoing digitalization of industry and the resulting flood of data require new, modern concepts for structuring and managing this data. The phrase “data is the new gold” is often quoted. However, this can only be true if the data is available for analysis at the right time, in the right format and with the right context.

In this course, students will learn how to set up a modern data architecture that meets all the requirements of Industry 4.0. They will learn which components are required for this and how to use these components correctly.

Technical and methodological competence (F / M)

• Students can name the challenges associated with Industry 4.0 and describe common solutions.
• Students can define and explain the term Unified Namespace (UNS) in the context of Industrie 4.0.
• Students can create and implement a UNS namespace concept
• Students know how an MQTT broker and the different quality-of-service levels work
• Students know the most important data types and commands of a Redis store.
• Students know how a message queue works and understand how it can be used to decouple applications from each other.
• Students can explain the concepts of "data mesh" and "data product" and classify them in the context of Industry 4.0.
• Students understand how a time series database works and how it differs from a classic relational database.
• Students understand the concepts behind continuous aggregates and are able to implement them.
• Students can independently create and configure simple Grafana dashboards to visualize the collected data

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

• Students can solve set tasks independently and on time (reliability).
• Students can summarize information and present it in a manner appropriate to the target group (expressiveness and appearance).
• Students understand the solutions of others and can make constructive suggestions for improvement and deal with feedback (critical ability) and reflect on their own abilities and limitations (self-reflection ability).
• Ability and willingness to acquire new knowledge independently and to learn from successes and failures (learning competence and motivation).

Lectures and Exercises

Assessment of exercises 50%
Assessment of a presentation on a selected topic of the course 50%

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

Not applicable

Salcher, F., Finck, S., & Hellwig, M. (2024). A smart shop floor information system architecture based on the unified namespace. In 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC) (pp. 1-9). IEEE.
Strengholt, P. (2023). Data management at scale. " O'Reilly Media, Inc.".
Hirsch, E., Hoher, S., & Huber, S. (2023). An OPC UA-based industrial Big Data architecture. arXiv. http://arxiv.org/abs/2306.01418
Kandasamy, J., Muduli, K., Meena, P. L., & Kommula, V. P. (Eds.). (2022). Smart manufacturing technologies for industry 4.0: integration, benefits, and operational activities. CRC Press.
Raj, A., Dwivedi, G., Sharma, A., Lopes de Sousa Jabbour, A. B., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546. https://doi.org/10.1016/j.ijpe.2019.107546
Trunzer, E., Calà, A., Leitão, P., Gepp, M., Kinghorst, J., Lüder, A., Schauerte, H., Reifferscheid, M., & Vogel-Heuser, B. (2019). System architectures for Industrie 4.0 applications: Derivation of a generic architecture proposal. Production Engineering, 13(3–4), 247–257. https://doi.org/10.1007/s11740-019-00902-6
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006

Classroom teaching, attandance is mandatory