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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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You are here: Home Teaching SS 21 Semantic Data Integration

Semantic Data Integration

Type Practical course (advanced level)
Term SS 21

In the lab course, students will use a selection of state-of-the-art data management technologies including big data frameworks such as Apache Spark or Apache Kafka, architectures for data and service ecosystems such as International Data Spaces or GAIA-X, modeling languages such as OWL, and collaborative data modeling environments such as Mobi to develop advanced data integration workflows that integrate data from multiple sources and enrich it with semantic data. Various use cases will be studied during the course. Recommended prerequisites: Students should have detailed knowledge about database systems (e.g., course Implementation of Databases or another advanced course on database systems) and data modeling techniques (at least be familiar with conceptual modeling in UML, ER or similar formlisms).

Learning Objectives

  • Students are able to design complex data integration workflows
  • Students learn to create data management applications with state-of-the-art big data technologies
  • Students can handle semantic technologies to enrich data management applications
  • Students understand the complexity of data integration processes
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