Refine
Year of publication
Document Type
- Conference Proceeding (8)
- Article (unreviewed) (3)
- Part of a Book (2)
- Book (1)
- Report (1)
Conference Type
- Konferenzartikel (5)
- Konferenz-Abstract (2)
- Sonstiges (1)
Has Fulltext
- no (15)
Is part of the Bibliography
- yes (15)
Keywords
- ERP (2)
- Wirtschaftsinformatik (2)
- Anpassung (1)
- Artificial Intelligence (1)
- Business Intelligence (1)
- Dimensional Modelling (1)
- Geschäftsführung (1)
- In-Memory Technologie (1)
- KI-Labor Südbaden (1)
- Konferenz (1)
Institute
Open Access
- Open Access (8)
- Closed Access (3)
- Bronze (2)
- Closed (1)
- Diamond (1)
Machine learning (ML) has become highly relevant in applications across all industries, and specialists in the field are sought urgently. As it is a highly interdisciplinary field, requiring knowledge in computer science, statistics and the relevant application domain, experts are hard to find. Large corporations can sweep the job market by offering high salaries, which makes the situation for small and medium enterprises (SME) even worse, as they usually lack the capacities both for attracting specialists and for qualifying their own personnel. In order to meet the enormous demand in ML specialists, universities now teach ML in specifically designed degree programs as well as within established programs in science and engineering. While the teaching almost always uses practical examples, these are somewhat artificial or outdated, as real data from real companies is usually not available. The approach reported in this contribution aims to tackle the above challenges in an integrated course, combining three independent aspects: first, teaching key ML concepts to graduate students from a variety of existing degree programs; second, qualifying working professionals from SME for ML; and third, applying ML to real-world problems faced by those SME. The course was carried out in two trial periods within a government-funded project at a university of applied sciences in south-west Germany. The region is dominated by SME many of which are world leaders in their industries. Participants were students from different graduate programs as well as working professionals from several SME based in the region. The first phase of the course (one semester) consists of the fundamental concepts of ML, such as exploratory data analysis, regression, classification, clustering, and deep learning. In this phase, student participants and working professionals were taught in separate tracks. Students attended regular classes and lab sessions (but were also given access to e-learning materials), whereas the professionals learned exclusively in a flipped classroom scenario: they were given access to e-learning units (video lectures and accompanying quizzes) for preparation, while face-to-face sessions were dominated by lab experiments applying the concepts. Prior to the start of the second phase, participating companies were invited to submit real-world problems that they wanted to solve with the help of ML. The second phase consisted of practical ML projects, each tackling one of the problems and worked on by a mixed team of both students and professionals for the period of one semester. The teams were self-organized in the ways they preferred to work (e.g. remote vs. face-to-face collaboration), but also coached by one of the teaching staff. In several plenary meetings, the teams reported on their status as well as challenges and solutions. In both periods, the course was monitored and extensive surveys were carried out. We report on the findings as well as the lessons learned. For instance, while the program was very well-received, professional participants wished for more detailed coverage of theoretical concepts. A challenge faced by several teams during the second phase was a dropout of student members due to upcoming exams in other subjects.
This paper describes the concept and some results of the project "Menschen Lernen Maschinelles Lernen" (Humans Learn Machine Learning, ML2) of the University of Applied Sciences Offenburg. It brings together students of different courses of study and practitioners from companies on the subject of Machine Learning. A mixture of blended learning and practical projects ensures a tight coupling of machine learning theory and application. The paper details the phases of ML2 and mentions two successful example projects.
The need for the logistics sector to timely respond to the increasing requirements of a globalised and digitalised world relies greatly on the com- petences and skills of its labour force. It becomes therefore essential to reinforce the cooperation between universities and business partners in the logistics and supply chain management fields across the European region and to build a logistics knowledge cluster supported by a communication and collaboration platform to foster continuous learning, skill acquisition and experience sharing anytime anywhere. In this paper we focus on designing the conceptual and technical framework for a communication and collaboration platform with the aim to establish the communication pipelines between the partner institutions, facilitating user interactions and exchange, leading to the creation of new knowledge and innovation in the logistics field. This framework is based on the requirements of the three main stakeholders: students, lecturers and companies, and consists of four functional areas defined according to the platform opera- tional requirements. A working prototype of the platform was developed using the Moodle learning management system and its core tools to determine its applicability and possible enhancement requirements. In the next stages of the project some additional tools like a knowledge base and the integration of the partners’ learning management systems to form the logistics knowledge cluster will be implemented.
Artificial Intelligence (AI) can potentially transform many aspects of modern society in various ways, including automation of tasks, personalization of products and services, diagnosis of diseases and their treatment, transportation, safety, and security in public spaces, etc. Recently, AI technology has been transforming the financial industry, offering new ways to analyse data and automate processes, reduce costs, increase efficiency, and provide more personalized services to customers. However, it also raised important ethical and regulatory questions that need to be addressed by the industry and society as a whole. The aim of the Erasmus+ project Transversal Skills in Applied Artificial Intelligence - TSAAI (KA220-HED - Cooperation Partnerships in higher education) has been to establish a training platform that will incorporate teaching guidelines based on a curriculum covering different areas of application of AI technology. In this work, we will focus on applying AI models in the financial and insurance sectors.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.
SAP S/4HANA, das neue ERP-System der SAP SE, wird einem Funktionscheck im Bereich des Produktionscontrollings unterzogen. Ermittelte Anforderungen an die IT-Unterstützung eines modernen Produktionscontrolling-Konzeptes werden auf ihre Umsetzbarkeit mit SAP S/4HANA evaluiert und anschließend in einem realitätsnahen End-to-End-Szenario implementiert. Im aktuellen Release-Stand treten an mehreren Stellen noch funktionale Lücken auf, die nur über den Rückgriff auf Technologien und Oberflächen des Vorgängers SAP ECC geschlossen werden können.
This book has emerged from lectures and courses given in recent years by the authors at their universities and shows how theoretical concepts of Business Intelligence are applied in SAP BW on HANA.
The authors developed a set of case studies guiding the student through the complete process of building an end-to-end BI system, based on a simple but realistic business scenario. The cases are designed in such a way that the application of many concepts such as staging, core data warehouse, data mart, reporting, etc., in SAP BW on HANA is introduced and demonstrated step by step.
Target Audience:
The cases are primarily designed for SAP BW beginners, who want a first introduction and hands-on experience with the latest version of BW on HANA. We briefly touch the general concepts of Business Intelligence and Data Warehousing. These concepts are discussed in many excellent books out in the market, which we don’t want to replace. The reader should either already be familiar with these concepts or should be willing to use the references we provide. Also, this book can NOT replace a complete consultant training for BW, but it can serve as a starting point for a journey into the world of SAP BW on HANA.