Content, progress and pedagogy of the module

Learning objectives

Knowledge

Students should achieve knowledge on the following topics in data-intensive systems:

• concepts and techniques for analyzing large data volumes, such as data warehousing, On-Line Analytical Processing, and Data Mining
• concepts and techniques for handling spatio-temporal data, including indexing and processing of queries
• concepts and techniques for scalability for data-intensive systems, e.g., cloud computing

Topics will typically be exemplified by Internet-related application, such as web analytics, spatial web, and the like.

There will also be one or more optional subjects within data-intensive systems, including but not limited to:

• concepts and techniques for managing web-related data such as XML, Semantic Web, and Web2.0 data
• concepts and techniques for search engines

Skills

• be able to explain concepts and techniques in data-intensive systems
• be able to select and apply relevant concepts and techniques for a given problem in data-intensive systems

Competences

• be able to apply concepts and techniques from data-intensive systems, including design and implementation of data-intensive systems.

Type of instruction

The teaching is organized according to the general teaching methods for the education, cf. chapter 3

Extent and expected workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours

Exam

Exams

Additional information

Contact: The Study board for Computer Science at cs-sn@cs.aau.dk or 9940 8854