Projects

Besides tutorials and worksheets to be posted publicly at the end of the semester, there will be some projects assigned to students. The best way to learn is by doing, so these will largely be applied assignments that provide hands-on experience with the basic skills taught during the lab.

You will have to choose one out of possible proposed projects. Each project is designed for group work and recommended to be worked on in groups of 3-4 students. It’s part of the project to work in a team.

1. Particle Swarm Optimization using Spark framework

2. Entity Linking Across Knowledge Graphs using Spark

3. OWL/XML Parser using Spark

4. Translation Embedding for modellingMulti-Relational Data using Spark

5. DISTMULT Modelfor Multi-Relational Representation Learning using Intel BigDL over Spark framework

Grading

Grades for all projects will be assessed as follows:

• project and team selection, problem understanding, implementation concept, and pre-presentation (15%)
• project submission (implementation, documentation, project report) (80%)
  • implementation (40%)
  • project report (40%)
    • motivation, documentation (20%)
    • results and discussion (20%)
  • due 11/07/2019 (11:59pm – no extension!!)
  • submit report and code via Git repository commit
• Q&A session (5%)