Conference Proceeding
Refine
Year of publication
Document Type
- Conference Proceeding (68) (remove)
Keywords
- Telekommunikationsmarkt (6)
- Führung (4)
- Leadership (4)
- Motivation (2)
- Self-Leadership (2)
- regulation (2)
- Active learning (1)
- Breitband Markt (1)
- Bundesnetzagentur (1)
- Business Process Intelligence (1)
- Case Study (1)
- Challenges (1)
- Coaching (1)
- Communication (1)
- Deep learning (1)
- Deutscher Dialogmarketing-Preis (1)
- Deutscher Direktmarketing-Verband (1)
- Digital start-up (1)
- Digitalisierung (1)
- Direktmarketing (1)
Institute
- Fachbereich Wirtschaftswissenschaften (68) (remove)
Bitcoin is a cryptocurrency and is considered a high-risk asset
class whose price changes are difficult to predict. Current research focusses
on daily price movements with a limited number of predictors. The paper at
hand aims at identifying measurable indicators for Bitcoin price movement s
and the development of a suitable forecasting model for hourly changes. The
paper provides three research contributions. First, a set of significant
indicators for predicting the Bitcoin price is identified. Second, the results of
a trained Long Short-term Memory (LSTM) neural network that predicts
price changes on an hourly basis is presented and compared with other
algorithms. Third, the results foster discussions of the applicability of neural
nets for stock price predictions. In total, 47 input features for a period of
over 10 months could be retrieved to train a neural net that predicts the
Bitcoin price movements with an error rate of 3.52 %.
Leveraging Social Network Data for Analytical CRM Strategies - The Introduction of Social BI.
(2012)
Introduction of RePriCo’13
(2013)
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.