Institut für Wirtschaftsinformatik

Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/66

Listen

Ergebnisse nach Hochschule und Institut

Gerade angezeigt 1 - 10 von 16

Checking the students aptitude for a bachelor program: experiences with a web-based tool
(2018) Jüngling, Stephan; Telesko, Rainer; Reber, Andreas
Research Abstract: Checking the student’s aptitude for a Bachelor program: Experiences with a Web-based tool Research Objectives: In autumn 2014 the Bachelor program „Business Information Technology (BIT)” has been launched. BIT is about the application of information technology in business with the focus on building information systems. Since several terms, it can be observed that a considerable number of students faces difficulties in modules related to programming and mathematics at the beginning of the study. In order to monitor the aptitude of the program for beginners a project was launched with the aim to develop a method and a web-tool supporting the self-assessment related to indispensable competencies in the BIT program. Methodology: The aptitude test - built with Google Forms - currently consists of 31 predefined multiple choice questions and calculates an overall aptitude value and single aptitude values for the main categories logical and analytical thinking, understanding algorithms and abstract thinking. The questions are taken from well-established test systems like ELIGO-System, BOMAT, CASA etc. and are typically solved within less than 45 minutes. The students can check their suitability for the study programme by comparing their overall aptitude value with a given threshold. First test runs conducted with the tool confirm the validity of the aptitude test. The future scope will involve more students and deal with an analysis of concrete weaknesses that can be used as input to adapt the settings of programming and mathematics modules.
06 - Präsentation
Towards an assistive and pattern learning-driven process modeling approach
(2019) Laurenzi, Emanuele; Hinkelmann, Knut; Jüngling, Stephan; Montecchiari, Devid; Pande, Charuta; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter
The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
04B - Beitrag Konferenzschrift
Leverage white-collar workers with AI
(2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, Peter
Based on the example of automated meeting minutes taking, the paper highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible.
04B - Beitrag Konferenzschrift
Anomaly detection in railway infrastructure
(2021) Morandi, David; Jüngling, Stephan
In order to keep complex railway systems fail-safe, sophisticated maintenance of the rolling stock and infrastructure are most essential. Although AI-based predictive maintenance systems exist in many different industries, there is still quite large potential for different application scenarios. The current research shows such an example, where machine learning can be applied to detect anomalies in the pantograph-catenary system by using a simple convolutional neural network that is able to detect arc ignitions during train operation. The paper provides some insights into the process of the system development life cycle. Starting from the initial idea to use machine learning for anomaly detection, over the system design of a prototype and the training of the Keras-based machine-learning model, up until the evaluation of the conducted experiments. The arcVision system prototype provides valuable insights into how a predictive maintenance process could be established by combining the results from the machine-learning model with rules and insights from manual inspections.
04B - Beitrag Konferenzschrift
Combining symbolic and sub-symbolic AI in the context of education and learning
(2020) Telesko, Rainer; Jüngling, Stephan; Gachnang, Phillip; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.
04B - Beitrag Konferenzschrift
Towards AI-based solutions in the system development lifecycle
(2020) Jüngling, Stephan; Peraic, Martin; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
Many teams across different industries and organizations explicitly apply agile methodologies such as Scrum in their system development lifecycle (SDLC). The choice of the technology stack, the programming language, or the decision whether AI solutions could be incorporated into the system design either is given by corporate guidelines or is chosen by the project team based on their individual skill set. The paper describes the business case of implementing an AI-based automatic passenger counting system for public transportation, shows preliminary results of the prototype using anonymous passenger recognition on the edge with the help of Google Coral devices.It shows how different solutions could be integrated with the help of rule base systems and how AI-based solutions could be established in the SDLC as valid and cost-saving alternatives to traditionally programmed software components.
04B - Beitrag Konferenzschrift
Decision support combining machine learning, knowledge representation and case-based reasoning
(Sun SITE, Informatik V, RWTH Aachen, 2021) Mehli, Carlo; Hinkelmann, Knut; Jüngling, Stephan
Knowledge and knowledge work are essential for the success of companies nowadays. Decisions are based on knowledge and better knowledge leads to more informed decisions. Therefore, the management of knowledge and support of decision making has increasingly become a source of competitive advantage for organizations. The current research uses a design science research approach (DSR) with the aim to improve the decision making of a knowledge intensive process such as the student admission process, which is done manually until now. In the awareness phase of the DSR process, the case study research method is applied to analyze the decision making and the knowledge that is needed to derive the decisions. Based on the analysis of the application scenario, suitable methods to support decision making were identified. The resulting system design is based on a combination of Case-Based Reasoning (CBR) and Machine Learning (ML). The proposed system design and prototype has been validated using triangulation evaluation, to assess the impact of the proposed system on the application scenario. The evaluation revealed that the additional hints from CBR and ML can assist the deans of the study program to improve the knowledge management and increase the quality, transparency and consistency of the decision-making process in the student application process. Furthermore, the proposed approach fosters the exchange of knowledge among the different process participants involved and codifies previously tacit knowledge to some extent and provides relevant externalized knowledge to decision makers at the required moment. The designed prototype showcases how ML and CBR methodologies can be combined to support decision making in knowledge intensive processes and finally concludes with potential recommendations for future research.
04B - Beitrag Konferenzschrift
Towards context-oriented process modelling in the circular economy
(2021) Jüngling, Stephan; Kierans, Gordana; Ding, Zhuoqi; Bösch, Michael
In the linear economy model, Lean Management and Process Excellence were initially developed in the automotive industry but they were slowly adapted by service industries, such as banks or insurance companies to optimize their own Business Processes (BP). When optimizing BP, models are created and BPMN (Business Process Modelling Notation) serves as a standard notation to design and optimize BP with the help of well-known KPIs (Key Performance Indicators). However, the recent trend of considering the circular economy in an organization’s optimization initiatives has resulted in increasing pressure to put additional focus on environmental-friendly production processes. Consequently, business process models today should not only be optimized according to the principles of process excellence but also put more emphasis on design as a part of circular economy (CE) to gain environmental excellence. Thus, BPMN models need to become more context aware. The objective of this exploratory paper proposes varying ways to incorporate well-known aspects of CE into the methods, models and tools of Business Process Management in order to move towards a Context-Oriented Process Modelling in the Circular Economy. The aim is to contribute to the discussion on how additional measures from environmental, economic and financial incentives could generate an impact on how products and services should be designed in a human-centered and environmentally friendly Society 5.0.
04B - Beitrag Konferenzschrift
A human aptitude test for object-oriented programming in the context of AI and machine learning
(Springer, 2020) Jüngling, Stephan; Telesko, Rainer; Dornberger, Rolf
Many current IT systems are implemented based on the object-oriented (OO) programming paradigm, which over more than two decades has proved to be one of the most successful mechanisms for code re-use and the most powerful extension mechanisms used in many software components and systems. Combined with a solid understanding of business principles and good communication skills, OO is still considered to be one of the core skills in the design of platforms and systems that drive our current IT landscape. The self-evaluation test, which we developed as an early indicator for prospective Business Information Technology (BIT) students, revealed insights about the skill level of beginners and serves as a starting point to reflect on abstraction skills in the context of the current digitalization and the increase in artificial intelligence (AI) components. The article explains the relevance of OO thinking on different levels of abstraction in the context of the lifecycle of current system architectures and provides an outlook on how these abstraction skills can be re-used when switching from an OO development paradigm into a new area where AI and machine learning will steadily increase their influence on the overall design of software systems.
04A - Beitrag Sammelband
Ein menschlicher Eignungstest für objektorientierte Programmierung im Kontext von KI und maschinellem Lernen
(Springer, 2023) Telesko, Rainer; Jüngling, Stephan; Dornberger, Rolf
Viele aktuelle IT-Systeme werden auf der Grundlage des objektorientierten (OO) Programmierparadigmas implementiert, das sich in mehr als zwei Jahrzehnten als einer der erfolgreichsten Mechanismen für die Wiederverwendung und Erweiterung von Code erwiesen hat und in vielen Softwarekomponenten und -systemen verwendet wird. In Verbindung mit einem soliden Verständnis von Geschäftsprinzipien und guten Kommunikationsfähigkeiten gilt OO immer noch als eine der Kernkompetenzen bei der Entwicklung von Plattformen und Systemen, die unsere heutige IT-Landschaft bestimmen. Der Selbsteinschätzungstest, den wir als Frühindikator für angehende Studierende der Wirtschaftsinformatik (BIT) entwickelt haben, gibt Aufschluss über das Kompetenzniveau von Studienanfängern und -anfängerinnen und dient als Ausgangspunkt, um über Abstraktionsfähigkeiten im Kontext der aktuellen Digitalisierung und der Zunahme von Komponenten der künstlichen Intelligenz (KI) nachzudenken. Der Artikel erläutert die Relevanz des OO-Denkens auf verschiedenen Abstraktionsebenen im Kontext des Lebenszyklus aktueller Systemarchitekturen und gibt einen Ausblick darauf, wie diese Abstraktionsfähigkeiten beim Wechsel von einem OO-Entwicklungs-paradigma in einen neuen Bereich wiederverwendet werden können, in dem KI und maschinelles Lernen ihren Einfluss auf den Gesamtentwurf von Software-systemen stetig vergrößern werden.
04A - Beitrag Sammelband

Institut für Wirtschaftsinformatik

Listen

Filter

Einstellungen

Sortieren nach

Ergebnisse pro Seite

Ergebnisse nach Hochschule und Institut