004 Informatik
Refine
Year of publication
- 2013 (2) (remove)
Document Type
- Journal article (2)
Language
- English (2)
Has full text
- yes (2)
Is part of the Bibliography
- yes (2)
Institute
- Informatik (2)
Publisher
- IARIA (1)
"Learning by doing" in Higher Education in technical disciplines is mostly realized by hands-on labs. It challenges the exploratory aptitude and curiosity of a person. But, exploratory learning is hindered by technical situations that are not easy to establish and to verify. Technical skills are, however, mandatory for employees in this area. On the other side, theoretical concepts are often compromised by commercial products. The challenge is to contrast and reconcile theory with practice. Another challenge is to implement a self-assessment and grading scheme that keeps up with the scalability of e-learning courses. In addition, it should allow the use of different commercial products in the labs and still grade the assignment results automatically in a uniform way. In two European Union funded projects we designed, implemented, and evaluated a unique e-learning reference model, which realizes a modularized teaching concept that provides easily reproducible virtual hands-on labs. The novelty of the approach is to use software products of industrial relevance to compare with theory and to contrast different implementations. In a sample case study, we demonstrate the automated assessment for the creative database modeling and design task. Pilot applications in several European countries demonstrated that the participants gained highly sustainable competences that improved their attractiveness for employment.
When forecasting sales figures, not only the sales history but also the future price of a product will influence the sales quantity. At first sight, multivariate time series seem to be the appropriate model for this task. Nonetheless, in real life history is not always repeatable, i.e., in the case of sales history there is only one price for a product at a given time. This complicates the design of a multivariate time series. However, for some seasonal or perishable products the price is rather a function of the expiration date than of the sales history. This additional information can help to design a more accurate and causal time series model. The proposed solution uses an univariate time series model but takes the price of a product as a parameter that influences systematically the prediction based on a calculated periodicity. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. The periodicity is calculated based on a novel approach that is based on data folding and Pearson Correlation. Compared to other techniques this approach is easy to compute and allows to preset the price parameter for predictions and simulations. Tests with data from the Data Mining Cup 2012 as well as artificial data demonstrate better results than established sophisticated time series methods.