An introductory Webinar on applications and limitations of AI in economy and society.

Participants are (from left to right):

Prof. Robert Müller-Török, University of Public Administration and Finance, Ludwigsburg
Prof. Hiroko Kudo, Chuo University, Tokyo
Prof. Andras Nemeslaki, University of Technology and Economics, Budapest

Moderation:

Prof. Alexander Prosser, University of Economics and Business, Vienna

The webinar was recorded on Sept. 16, 2021 at the University of Public Service, Budapest within CEEeGov 2021.

This project was supported by Baden-Württemberg Stiftung, here a list of the project partners.

Project coordination, Europazentrum Baden-Württemberg

An introductory webinar with Prof. Robert Müller-Török, HVF Ludwigsburg.

Moderator: Irina Cojocaru, Information Society Development Institute Moldova

This project was supported by Baden-Württemberg Stiftung, here a list of the project partners.

Project coordination, Europazentrum Baden-Württemberg

Content

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 speech recognition and analysis.

Learning Outcomes

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 and enhance analytics. Furthermore, students will learn to analyse analogue data and to merge it with formatted data from commercial information systems, such as ERP. The analogue data used will be voice clips from a “helpdesk support”, which are analysed in speech recognition, assigned to topics talked about and the sentiment of the talk. This enables to analyse customer feelings about the company products, which are related to traditional analysis of formatted data, in this case customer interactions.

Formatted data analysis, however, will not be based on traditional data warehousing based on aggregate “cubes”, but will utilize in-memory computing to analyse individual records which enable a much more in-depth analysis.

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

For DFM modelling cf. Golfarelli, M., Rizzi, S., Maio, D., The Dimensional Fact Model: A Conceptual Model for Data Warehouses