Using thematic ontologies for user- and group- based adaptive personalization in web searching

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents

Anbindung eines Temperatur- und Luftdrucksensors an einen �System on Chip-Webserver� über SPI-Bus

Während der Windkanaltests werden die Umgebungsbedingungen im Windkanal gemessen und an einen Datenserver geschickt, um eine optimale Auswertung zu ermöglichen. Zu den Messungen gehören die Bestimmung des Drucks an drei verschiedenen Stellen, der Temperatur und der Luftfeuchte. Während der Vorbereitungen der Test in der Rotorhalle am Institut in Braunschweig werden diese Daten durch einen Mikrokontroller simuliert. Durch die Anbindung des Sensors für Luftdruck und Temperatur wird die Simulation des Windkanals um echte Messwerte erweitert, welche somit auch in der Vorbereitungszeit den Fehler in Abgeleiteten Daten reduzieren

A Reference Model to Support Risk Identification in Cloud Networks

The rising adoption of cloud computing and increasing interconnections among its actors lead to the emergence of network-like structures and new associated risks. A major obstacle for addressing these risks is the lack of transparency concerning the underlying network structure and the dissemination of risks therein. Existing research does not consider the risk perspective in a cloud network’s context. We address this research gap with the construction of a reference model that can display such networks and therefore supports risk identification. We evaluate the reference model through real-world examples and interviews with industry experts and demonstrate its applicability. The model provides a better understanding of cloud networks and causalities between related risks. These insights can be used to develop appropriate risk management strategies in cloud networks. The reference model sets a basis for future risk quantification approaches as well as for the design of (IT) tools for risk analysis

Full Semantics Preservation in Model Transformation – A Comparison of Proof Techniques

Model transformation is a prime technique in modern, model-driven software design. One of the most challenging issues is to show that the semantics of the models is not affected by the transformation. So far, there is hardly any research into this issue, in particular in those cases where the source and target languages are different.\ud \ud In this paper, we are using two different state-of-the-art proof techniques (explicit bisimulation construction versus borrowed contexts) to show bisimilarity preservation of a given model transformation between two simple (self-defined) languages, both of which are equipped with a graph transformation-based operational semantics. The contrast between these proof techniques is interesting because they are based on different model transformation strategies: triple graph grammars versus in situ transformation. We proceed to compare the proofs and discuss scalability to a more realistic setting.\u