Hinkelmann, Knut

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Hinkelmann
Vorname
Knut
Name
Hinkelmann, Knut

Filter

2010 - 201953
Filter zurücksetzen

Einstellungen

Enddatum: 2019 × Anfangsdatum: 2010 × Institut: Institut für Wirtschaftsinformatik ×

Suchergebnisse

Gerade angezeigt 1 - 10 von 53
  • Vorschaubild nicht verfügbar
    Publikation
    Reports of the AAAI 2019 Spring Symposium Series
    (American Association for Artificial Intelligence, 2019) Hinkelmann, Knut [in: AI Magazine]
    The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Digital Maturity Model - How companies can achieve sustainable competitiveness in the future and the digital age
    (edition gesowip, 2019) Fluri, Jasmin; Schwaferts, Dino; Verkuil, Arie Hans; Hinkelmann, Knut; Aeschbacher, Marc [in: Digitalisierung und andere Innovationsformen im Management]
    For companies to be sustainably successful in the digital age, there are certain preconditions they must fulfill to be able to react to customer expectations and offer appropriate products or services competitively. This ability to proactively provide proper products or services within an adequate period is called Digital Maturity. To reach Dig-ital Maturity, companies must establish four preconditions: Customer Knowledge, Strategic Management, Technical Capability, and Cultural Agility. Those preconditions are called pillars of Digital Maturity. This chapter explains, why Digital Maturity concerns the whole company and requires different prerequisites on the leadership- as well as on the technical-, organizational, and cultural side of a company. The Digital Maturity assessment of the University of Applied Sciences and Arts Northwestern Switzerland is introduced and explains how to set up and incrementally perform a Digital Maturity assessment inside a company to continuously improve the companies’ Digital Maturity.
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Digital business development & portfolio. How to develop and manage digital business streams
    (edition gesowip, 2019) Grüner, Peter; Schwaferts, Dino; Studer, David; Wild, Simon; Verkuil, Arie Hans; Hinkelmann, Knut; Aeschbacher, Marc [in: Digitalisierung und andere Innovationsformen im Management. Aktuelle Perspektiven auf die digitale Transformation von Unternehmen und Lebenswelten]
    “The internet is like a wave. You either learn to swim with it or you will sink.” (Bill Gates). With this quote, Gates already pointed to the development of a digital economy, a completely new economic model based on information technology, where we now live. On the one hand, these new opportunities in information and communication technology (ICT) are changing the way companies have to operate. On the other hand, they are also changing customer expectations in a rapid and continuous manner that transforms them into fast moving targets. Therefore, legacy as well as non-legacy businesses have to rethink their business models, predict customer expectations and have to focus on customer centricity as well as on their own uniqueness on the market. In other words, organizations have to be very agile: they have to enable flexibility in their business models and diversify their risks in a well-managed way to remain competitive in such a dynamic environment. For these reasons, business model innovation is a crucial ability to manifest one’s own inimitability. Furthermore, business model innovation enables companies to search for new business logics and groundbreaking ways to create and capture value for its stakeholders. Nevertheless, such an innovative approach is risky, primarily due to its uncertainty of commercialization. Therefore, companies should not replace existing business models, but assess them with regard to the risk of a digital disruption and start with potential new business models next. This also requires changes in the organizational structure and asks for new methods to manage such a complex system of various business model development directions in a well-controlled way. One approach to cope with this challenge could be the digital business portfolio derived at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW).
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Digital ecosystem. How companies can achieve a sustainable competitiveness in the future and the digital age
    (edition gesowip, 2019) Grieder, Hermann; Schwaferts, Dino; Verkuil, Arie Hans; Hinkelmann, Knut; Aeschbacher, Marc [in: Digitalisierung und andere Innovationsformen im Management. Aktuelle Perspektiven auf die digitale Transformation von Unternehmen und Lebenswelten]
    Companies today are still highly influenced by the management theories of the indus-trial age. To be successful in the future companies need to become part of digital eco-systems where partners and customers will be co-creators. Customers and employees’ expectations are changing, and managers need to re-evaluate traditional management methods to enable companies to successfully transition into the digital era. During the transition, we will see a change from sociotechnical systems to transitional constructs that enable co-creation and collaboration. Digital Arenas will allow companies to create value outside of their industry by collaborating with other companies through APIs; this will give rise to new business models everywhere.
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Startup-Unternehmen und Digitalisierung: Wie verwenden Startup-Unternehmen digitale Tools?
    (edition gesowip, 2019) Loosli, Christina; Philippi, Stefan; Verkuil, Arie Hans; Hinkelmann, Knut; Aeschbacher, Marc [in: Digitalisierung und andere Innovationsformen im Management. Aktuelle Perspektiven auf die digitale Transformation von Unternehmen und Lebenswelten]
    Wie Startup-Unternehmen mit der Digitalisierung umgehen, ob sie digitale Prozesse bereitwillig in ihr Geschäftsmodell integrieren, und insbesondere wie Startup-Unternehmen digitale Tools einsetzen und ob sie ihr Geschäftsmodell bewusst auf digitale Prozesse ausrichten, soll in diesem Artikel diskutiert werden.
    04A - Beitrag Sammelband
  • Vorschaubild
    Publikation
    Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation
    (2019) Hinkelmann, Knut [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    This research is about combination of data-driven and knowledge-based recommendations The research is made in an application scenario for whisky recommendation, where a guest chats with a recommender system. Preferences about taste are difficult to express and the knowledge about taste is tacit and thus can hardly be represented and used adequately. People or not aware of how to describe flavors in a standardized way and how to do a justified choice. This is because knowledge about taste is mainly tacit knowledge. To deal with this knowledge, data-driven recommendation is adequate. On the other hand, in particular experienced customers use knowledge about distilleries, locations and the distillery process to express their preferences and want to have arguments for the recommended products. This shows that a combination of data-driven and knowledge-based recommendations is appropriate in areas where tacit knowledge and explicit knowledge are available.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    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, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    We 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 Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
    (CEUR Workshop Proceedings, 2019) Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter
    03 - Sammelband
  • Vorschaubild
    Publikation
    Towards an assistive and pattern learning-driven process modeling approach
    (2019) Hinkelmann, Knut [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    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
  • Vorschaubild
    Publikation
    Leverage white-collar workers with AI
    (2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    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