Advanced Data Models – Summer Semester 2026

This repository contains materials for the Advanced Data Models lecture (SS2026) at the Applied University Esslingen. The course explores modern data models beyond traditional relational systems and their role in building scalable, AI‑ready architectures.

Repository structure

• report.md – Course guide covering philosophy, semester plan, topics, readings, demo ideas, lecture template, interactive activities, infrastructure, and grading rubric.
• groups/ – One subdirectory per group (e.g., group1-graph-databases/) containing slides, demo code, data and notebooks. See report.md Section 8 for the full directory layout.

Guidelines for students

• Form groups of 2 students and select a topic (graph databases, triple stores, vector databases, document stores, key‑value stores, wide‑column stores or data lakes).
• Deeply study the assigned paper or chapter, prepare your 180‑minute lecture using the template provided in report.md, and design an interactive activity and practical demo.
• Use open‑source or free‑tier tools that run on student laptops (e.g. Neo4j, Apache Jena, ChromaDB, MongoDB, Redis, Cassandra, DuckDB). Docker setup is encouraged.
• AI coding assistants (ChatGPT, Codex, Claude) may be used to accelerate code development, but every student must understand and be able to explain what their code does.
• Commit all your materials (slides, demo scripts, data, notebooks) to your group directory in this repository and be prepared to explain your design decisions during your lecture.

For full course philosophy, semester schedule, suggested readings, demo ideas and evaluation rubric, please consult report.md.