Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons
Marx Gómez, Jorge (Prüfungsberechtigt)
Lehrenden, Die im Modul (Prüfungsberechtigt)
Prerequisites
No participant requirement
Skills to be acquired in this module
Objective of the module/skills:
Current module provides basics of business intelligence with focus on enterprises and strong emphasis on data warehousing technologies. Students of the course are provided with knowledge, which reflects current research and development in a data analytic domain. Professional competence
The students:
name and recognize the role of business intelligence as past of daily business process
being able to analyse advantages and disadvantages of different approaches and methods of the data analytics and being able to apply them in simple case studies
obtain theoretical knowledge about data collection and modelling processes, including most applicable approaches and best practices
Methodological competence
The students:
being able to execute typical tasks of business intelligence, and also being able to deepen knowledge on different approaches and methods
gain a hans on experience and being able to understand advantages and disadvantages of different methods and being able to use obtained knowledge in most efficient ways
Social competence
The students:
build solutions based on case studies given to the group, for example solving the issue of a factless fact table
discuss solutions on a technical level
present obtained case studies solutions as part of the exercises
Self-competence
The students:
critically review provided data and information
Module contents
Data warehouse technology together with business intelligence are increasingly being used by business in order to get better decision support and enrich ongoing rocesses with data-rich decisions. Data warehouse technology enables an integration of data from heterogeneous sources, whether business intelligence builds data rocessing on top of it. For instance, business intelligence allows to build reporting on very large volumes of data (including historical) coming primary from data warehouse. As past of the current module following contents are taught:
Definition and scope of business intelligence.
Procedures and objectives of data warehousing.
Process of extracting, transforming and loading (ETL) of data.
Phases of data modelling, data capturing and reporting in conjunction with a plausible case studies/scenarios.
Prospects for further and evolving topics for business intelligence (e.g. Adaptive Business Intelligence, In-MemoryComputing. etc.)
Introduction to Data Mining.
Case studies based practical exercises and assessments in order to impart practical knowledge.
Recommended reading
Marx Gómez, Rautenstrauch, Cissek (2008): Einführung in die Business Intelligence mit SAP NetWeaver 7.0.
Marx Gómez, Rautenstrauch, Cissek, Grahlher (2006): Einführung in SAP Business Information Warehouse, Springer, Heidelberg.
Moss, Atre (2006): Business Intelligence Roadmap, Addison-Wesley, Boston.
Loshin (2003): Business Intelligence, Kaufmann, Amsterdam.
Müller, Lenz (2013): Business Intelligence.
Sabherwal, Becerra-Fernandez (2010): Business Intelligence: Practices, Technologies, and Management
