 |
| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2021S |
| Ziele |
(*)Students know about advanced concepts and techniques for management and processing of big data by bridging theory and practice. Students have in-depth knowledge about the current state of the art in this highly active and diverse field of research and have gained a deep understanding of the often well-established and longstanding theories underlying the huge variety of upcoming big data systems and tools. Overall, students think about big data systems and tools in new ways — not just how they work, but rather why they were designed that way and how to select appropriate systems and tools for a certain problem at hand.
|
| Lehrinhalte |
(*)- Foundations of NoSQL Data Management: Reliable, Scalable and Maintainable Data-Intensive Applications; NoSQL Data Models and Query Languages; NoSQL Data Modeling
- Distributed Data in NoSQL Systems: Replication, Partitioning, Transactions, Consistency and Consensus
- Derived Data in NoSQL Systems: Batch Processing, Stream Processing, Lambda vs. Kappa Architectures, Situation Assessment Techniques, Situation & Process Mining
- Queries in Computational Data Analytics: Query Languages & Execution (Index Structures, Similarity Queries)
- Natural Language Processing and Social Media Mining on the Web: Web Search, Web Extraction and Mining, Question Answering and Dialogue Systems
|
| Beurteilungskriterien |
(*)Exercises and written exam at the end of the semester.
|
| Lehrmethoden |
(*)Slide presentation with case studies and hands-on sessions.
|
| Abhaltungssprache |
English |
| Literatur |
(*)- Martin Kleppmann “Designing Data-Intensive Applications – The Big Ideas Behind Reliable, Scalable, and Maintainable Systems”, O'Reilly, March 2017
- Lena Wiese, “Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases”, De Gruyter/Oldenburg, 2015
- Kay Uwe Sattler, Gunter Saake and Erhard Rahm, “Verteiltes und Paralleles Datenmanagement – Von verteilten Datenbanken zu Big Data und Cloud”, Springer, 2015
- Nathan Marz and James Warren. “Big Data: Principles and Best Practices of Scalable Realtime Data Systems”, Manning Publications Co., Greenwich, CT, USA, 2015
- Wil van der Aalst, “Process Mining – Data Science in Action”, Springer, 2016.
- Ricardo Baeza-Yates, Berthier Ribeiro-Neto. “Modern Information Retrieval”, Addison-Wesley 2011
- Bruce Croft, David Metzler, Trevor Strohma. “Search Engines”, Pearson 2009
|
| Lehrinhalte wechselnd? |
Nein |
|
