Publikationen

Scientific progress in the area of machine learning, in particular advances in deep learning, have led to an increase in interest in eScience and related fields. While such methods achieve great results, an in-depth understanding of these new technologies and concepts is still often lacking and domain knowledge and subject matter expertise play an important role. In regard to space science there are a vast variety of application areas, in particular with regard to analysis of observational data. This chapter aims at introducing a number of promising approaches to analyze time series data, via the introduction query by example, i.e., any signal can be provided to the system, which then responds with a ranked list of datasets containing similar signals. Building on top of this ability the system can then be trained using annotations provided by expert users, with the goal of detecting similar features and hence provide a semiautomated analysis and classification. A prototype built to work on MESSENGER data based on existing background implementations by the Know-Center in cooperation with the Space Research Institute in Graz is presented. Further, several representations of time series data that demonstrated to be required for analysis tasks, as well as techniques for preprocessing, frequent pattern mining, outlier detection, and classification of segmented and unsegmented data, are discussed. Screen shots of the developed prototype, detailing various techniques for the presentation of signals, complete the discussion.

This paper presents a hybrid model for the prediction of magnetostriction in power transformers by leveraging the strengths of a data-driven approach and a physics-based model. Specifically, a non-linear physics-based model for magnetostriction as a function of the magnetic field is employed, the parameters of which are estimated as linear combinations of electrical coil measurements and coil dimensions. The model is validated in a practical scenario with coil data from two different suppliers, showing that the proposed approach captures the different magnetostrictive properties of the two suppliers and provides an estimation of magnetostriction in agreement with the measurement system in place. It is argued that the combination of a non-linear physics-based model with few parameters and a linear data-driven model to estimate these parameters is attractive both in terms of model accuracy and because it allows training the data-driven part with comparably small datasets.

Proceedings of the Workshop Papers of i-Know 2017, co-located with International Conference on Knowledge Technologies and Data-Driven Business 2017 (i-Know 2017), Graz, Austria, October 11-12, 2017.

Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.