Students will understand the concept, tools and limitations of in-memory-based business intelligence, which enables analytics far beyond traditional data warehousing. They will also understand how methods of artificial intelligence interact with analytics. Two case studies will be processed:

(i)                  Process mining of a process in sales: Students build a system to check a defined process against “real” transaction data, whether business rules are followed or if there are deviations; and if so, whether there are patterns in these deviations.

(ii)                Image classification: Tires are to be automatically classified, whether they are suitable for refurbishment or not. Students build a system that learns from pre-classified data what suitable and unsuitable tires look like. A set of unclassified tires is then to be analysed. For each tire a digital passport is available. The result data of the classification (suitable/not suitable) and the passport data is used to build a data warehouse to find patterns indicating (non-)suitability.

Both applications, process mining and image classification – have become standard applications in a manufacturing environment.

Students will also learn how to conceptually plan such a data warehouse with particular reference to unformatted and analogue data sources and their analysis.

Students understand how to plan an analytics system including analysis of analogous data with AI. They also experience the implementation of two frequent use cases – process mining as well as image classification and analysis – with a world-market leading system, SAP BW/4Hana.

According to the examination regulation full attendance is intended for a PI.

The entire course will emulate a real-world warehouse implementation project from its early planning stages to final use. The system used will be SAP HANA as well as tools for image classification and analysis.

The two case studies will mostly be implemented in class and extensions to the case to be done in individual homework.

Assessment will be based on:

- Writen exam in data modelling for data warehouses

- Implementation of the case study in SAP HANA implemented in class.

- Implementation of another case study in homework.

all criteria in individual assessment and accounting for 33,3% of the overall assessment each.

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

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