Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules. - 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation. - 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
- instruction
- documentation
- examination
This course is about Data Engineering and Information Retrieval. It covers methods and technologies for managing, processing and analyzing potentially large and distributed data collections, including multi-model databases and NoSQL stores. And it covers also mastering data in unstructured form (full text search). The course consists of four parts: 1. Database management; 2. Data warehousing and data analytics (business intelligence); 3. Data integration including data synthesizing; and 4. Information Retrieval.
Prerequisites
- Relational Models, Relational Algebra
- Normalization
- SQL:92
- Transaction Processing, Concurrency Control
- Security in relational database systems
- Query optimization (btree indexes)
- RDBMS architectures
Learning Objectives
This module covers following important aspects of Data Engineering:
- Students understand the use of modern database technologies for processing and managing large and distributed data collections.
- Reaching beyond RDBMS, students learn about data structures (data types) and know which of these to use depending on the requirements and type of data available (polyglot persistence, multi-model databases).
- The students know NoSQL stores and selected cloud data stores.
- The students know methods and tools to integrate, to cleanse and to synthesize data.
- Students know how to deal with full text information using databases and search engines (information retrieval).
- The students can also apply the acquired knowledge in their own working environment.
Contents of Module
The lecture is divided into four parts:
- Database Management (DB): New data structures and alternatives to RDBMS. The first part deals with the storage of data and with the non-relational aspects, including NoSQL and cloud data stores
- Data Warehousing and Data Analytics (DW): The second part deals with data warehousing, i.e. data aggregation and data analytics (business intelligence).
- Data Integration (DI): In the third part, methods and tools for data integration, data cleansing and data synthesizing (e.g. for training and testing) are explained.
- Information Retrieval (IR): The fourth part deals with finding information in full text using databases and (enterprise) search engines, including crawling..
Weighting:
- DB: 4 - 6 weeks
- DW: 3 weeks
- DI: 1 week
- IR: 4 - 5 weeks
Teaching and Learning Methods
Frontal teaching, exercises, case studies.
Literature
Optional literature suggestion (books):
- DB: Lena Wiese: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. De Gruyter Textbook. 2015. ISBN 978-3-11-044140-6.
- IR: "Modern Information Retrieval". Baeza-Yates & Ribeiro-Neto, New York (2011). ISBN: 9780321416919.
- IR: Introduction to Information Retrieval. C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, 2008. Classical and web information retrieval systems: algorithms, mathematical foundations and practical issues.
- IR: Information Retrieval in Practice. B. Croft, D. Metzler, T. Strohman. Pearson Education, 2009.
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