Institut für Wirtschaftsinformatik
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/66
Listen
597 Ergebnisse
Ergebnisse nach Hochschule und Institut
Publikation How to support customer segmentation with useful cluster descriptions(Springer, 2015) Witschel, Hans Friedrich; Loo, Simon; Riesen, Kaspar; Perner, PetraCustomer or market segmentation is an important instrument for the optimisation of marketing strategies and product portfolios. Clustering is a popular data mining technique used to support such segmentation – it groups customers into segments that share certain demographic or behavioural characteristics. In this research, we explore several automatic approaches which support an important task that starts after the actual clustering, namely capturing and labeling the “essence” of segments. We conducted an empirical study by implementing several of these approaches, applying them to a data set of customer representations and studying the way our study participants interacted with the resulting cluster representations. Major goal of the present paper is to find out which approaches exhibit the greatest ease of understanding on the one hand and which of them lead to the most correct interpretation of cluster essence on the other hand. Our results indicate that using a learned decision tree model as a cluster representation provides both good ease of understanding and correctness of drawn conclusions.04B - Beitrag KonferenzschriftPublikation E-Commerce Report Schweiz 2019. Digitalisierung im Vertrieb an Konsumenten. Eine qualitative Studie aus Sicht der Anbieter(Institut für Wirtschaftsinformatik, Hochschule für Wirtschaft FHNW, 2019) Wölfle, Ralf; Leimstoll, UweDie elfte Ausgabe der Studienreihe E-Commerce Report Schweiz behandelt neben der jüngsten Marktentwicklung die strukturellen Veränderungen in der Distribution von Konsumgütern. Das Vertiefungsthema 2019 ist ein Ausblick auf die vernetzte Angebotswelt im Jahr 2025.05 - Forschungs- oder ArbeitsberichtPublikation 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, PeterThe 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 KonferenzschriftPublikation A service based architecture for situation-aware adaptive event stream processing(2018) Schaaf, MarcThis paper presents the central aspects of a service based architecture for a distributed event stream processing system with an emphasis on its components, as well as related scalability and flexibility considerations. The processing system architecture is designed based on a well-defined situation-aware adaptive event stream processing model and a matching scenario definition language, which allow the definition of such processing scenarios in a processing system independent way.04B - Beitrag KonferenzschriftPublikation Leverage white-collar workers with AI(2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, PeterBased 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 KonferenzschriftPublikation Combining machine learning with knowledge engineering to detect fake news in social networks - A survey(2019) Ahmed, Sajjad; Hinkelmann, Knut; Corradini, Flavio; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, FrankDue to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.04B - Beitrag KonferenzschriftPublikation Towards a microservices-based distribution for situation-aware adaptive event stream processing(2019) Schaaf, MarcThis paper presents the central concepts for a microservices-based distribution of event stream processing pipelines as they are part of our situation-aware event stream processing system. For this, we outline changes to our specification language for a clear separation of the stream processing specification from the actual stream processing engine. Based on this separation, we then discuss our mapping approach for the assignment of the pipelines to stream processing nodes.04B - Beitrag KonferenzschriftPublikation Efficiency of multimodal hinterland terminals(2018) Ruile, HerbertHinterland terminals (HLT) are identified as key resources of inter-modality. However, between the sea port and Hinterland, there is an emerging complex infrastructure coined by heavily discontinuities and a broad range of services. This explorative, multi-case study investigates the information flow within the socio-technical system of HLTs.04B - Beitrag KonferenzschriftPublikation Learning and engineering similarity functions for business recommenders(2019) Witschel, Hans Friedrich; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, PeterWe study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedback. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.04B - Beitrag KonferenzschriftPublikation Data protection impact assessment guidelines in the context of the general data protection regulation(2019) Grütter, Bodo Jeremy; Schneider, Bettina; Dermol, ValerijThe European General Data Protection Regulation (EU GDPR) requires companies to carry out a so-called Data Protection Impact Assessment (DPIA) if the processing of personal data is likely to result in a high risk to the rights and freedoms of individuals. But how can it be determined whether a risk should be considered ‘high’ and thus makes a DPIA necessary? Furthermore, if a DPIA is required, how exactly should this be performed? In response to these questions, various guidelines concerning DPIA have been published. The aim of this paper is to give those affected by the new Data Protection law an insight into three current DPIA guidelines and to support them in implementing a GDPR-compliant impact assessment. To this end, each of the selected guidelines will be described, and evaluated in terms of GDPR compliance and DPIA feasibility, i.e. on the one hand, whether the guideline complies with the relevant GDPR articles, and on the other hand what tools are provided to facilitate the operational execution of a DPIA. The study results in an overall evaluation matrix, which shows that all three guidelines have different strengths and propose differing methods for DPIA implementation.04B - Beitrag Konferenzschrift