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Um einen zuverlässigen kontext-sensitiven Sicherheitsdienst bereitzustellen, ist die Vollständigkeit des zur Bewertung genutzten Sicherheitskontextes von wesentlicher Bedeutung. Der Anwendungskontext leistet dazu einen fundamentalen Beitrag. Aufgrund der fehlenden Interpretationslogik ist allerdings der Anwendungskontext von aussen nicht ermittelbar. Die Integration einer den Sicherheitsdienst unterstützenden Komponente in die Anwendung schafft hierzu auf zweierlei Weise Abhilfe. Sie stellt Anwendungskontextinformationen zur Verfügung und gewährleistet effektiv eine kontext-sensitive Sicherheitsadaption.
Der erste Teil dieser Arbeit gibt einen Überblick über die Themenfelder der modellgetriebenen Softwareentwicklung und der objektrelationalen Abbildung. Durch eine Kombination dieser beiden Themen wird schließlich der Begriff der modellgetriebenen O/R-Mapping-Frameworks definiert und näher erläutert. Im zweiten Teil bestätigt ein Vergleich von drei dieser Frameworks (Bold for Delphi, MDriven sowie Texo mit EclipseLink) die Vor- und Nachteile des modellgetriebenen Ansatzes auch in Bezug auf die Persistenz. Der Vergleich macht außerdem deutlich, was aktuell in der Praxis möglich ist (und was nicht) und in welchem Umfang Standards genutzt werden (insbesondere MDA und UML). Daneben werden auch die Schwächen in diesem Bereich aufgezeigt. Abschließend gibt es eine kurze Bewertung der Frameworks, auch im Hinblick auf mögliche Anwendungsszenarien.
Machbarkeitsanalyse über den Aufbau eines Enterprise Data Warehouse auf Basis von Apache Hadoop
(2016)
Die vorliegende Masterthesis liefert eine Einführung in die Themen Data Warehouse, Big Data und Apache Hadoop. Sie präsentiert grundlegende Data-Warehouse-Kon-zepte und überprüft, inwieweit diese mit dem Apache Hadoop Software Framework zu realisieren sind. Hierbei wird sowohl eine technische Überprüfung vorgenommen als auch verschiedene Szenarien veranschaulicht, wie Hadoop inhaltlich sinnvoll in bestehende Systeme integriert werden kann. Inhaltlich wird über das Thema Big Data an die Notwendigkeit einer solchen Überprüfung herangeführt.
M&A-Transaktionen werden selten so erfolgreich, wie sie geplant wurden. Dies wirft immer wieder die Frage auf, ob die Entscheidung für die Transaktion eine „gute“, d. h. rationale Entscheidung war und ob der Prozess auf optimale Weise geführt wurde. Im Rahmen der Studie wurden Experten aus 24 DAX-30-Unternehmen zum Thema „Rationalität und Irrationalität im M&A-Prozess“ befragt. Wunsch und Wirklichkeit fallen demnach deutlich auseinander und gerade Faktoren, die die Rationalität in einem Prozess erhöhen sollen, können diese in eine ungewollte Richtung treiben: Wachstumsstrategie, Anreizsysteme, Gremienentscheidungen, Bewertungsmodelle, auch Eigeninteressen der Akteure und Emotionen spielen eine große Rolle. Die Experten bestätigen insbesondere die hohe Relevanz verhaltenstheoretischer Phänomene wie den Planungsfehlschluss, den Herdeneffekt, oder die Selbstüberschätzung im M&A-Prozess.
M&A-Transaktionen werden selten so erfolgreich, wie sie geplant wurden. Dies wirft immer wieder die Frage auf, ob die Entscheidung für die Transaktion eine „gute“, d. h. rationale Entscheidung war und ob der Prozess auf optimale Weise geführt wurde. Im Rahmen der Studie wurden Experten aus 24 DAX-30-Unternehmen zum Thema „Rationalität und Irrationalität im M&A-Prozess“ befragt. Wunsch und Wirklichkeit fallen demnach deutlich auseinander und gerade Faktoren, die die Rationalität in einem Prozess erhöhen sollen, können diese in eine ungewollte Richtung treiben: Wachstumsstrategie, Anreizsysteme, Gremienentscheidungen, Bewertungsmodelle, auch Eigeninteressen der Akteure und Emotionen spielen eine große Rolle. Die Experten bestätigen insbesondere die hohe Relevanz verhaltenstheoretischer Phänomene wie den Planungsfehlschluss, den Herdeneffekt, oder die Selbstüberschätzung im M&A-Prozess.
The rising worldwide energy demand leads to the depletion of fossil fuels reserves and at the same time, it increases the environmental impact caused by emissions of greenhouse gases (GHG).
Utilization of fossil fuels causes not only climate change impacts such as global warming, but also many other environmental problems such as water and soil contamination that pose potential risks to human and animal health.
Furthermore, increasing population growth leads to increased food demand and consumption. This upward trend creates competition between food and bioenergy markets. Hence, the so‐called “food or fuel” discussion is back.
Challenges to counteract deciding between food and fuel that focus on the need to produce sustainable energy, while protecting environment, are the keys to replacing fossil fuels and lowering their greenhouse gas emissions. For this purpose, a completely new strategy with a proper sustainable system to supplying world’s energy demand must be found.
When it comes to web applications and their dynamic content, one seemingly common trouble area is that of layouts. Frequently, web designers resort to frameworks or JavaScript-based solutions to achieve various layouts where the capabilities of Cascading Style Sheets (CSS) fall short. Although the World Wide Web Consortium (W3C) is attempting to address the demand for more robust and concise layout solutions to handle dynamic content with the recent and upcoming specifications, a generic approach to creating layouts using constraint syntax has been proposed and implementations have been created. Yet, the introduction of constraint syntax would change the CSS paradigm in a fundamental way, demanding further analysis to determine the viability of its inclusion in core web standards. This thesis focuses on one particular aspect of the introduction of constraint syntax: how intuitive constraint syntax will be for designers. To this end, an experiment is performed involving participants thinking aloud while reading code snippets. Also, cursor movements are recorded as a proxy for eye movement over the code snippets. The results indicate that, upon first-impression, constraint syntax within CSS is not intuitive for designers.
Intelligent use of energy is one of the keys to success for an energy revolution. To meet this challenge, smart meters are suitable tools because INTELLIGENT use of energy means not only to use efficiency technology, but also to determine load shifting potentials and use them accordingly. Especially farms with high power consumption are becoming increasingly concerned about reducing energy costs due to rising energy prices and need a systematic analysis of their operational energy flow. To find solutions for farms, the NaRoTec e.V., the TH Köln, and the Machinery Ring Höxter-Warburg have joined forces with partners and launched the project "Intelligent Energy in Agriculture", which is funded by the state of NRW in Germany. The aim of the project is to be able to give individual advice recommendations for energy optimization of agricultural holdings. This will be achieved inter alia through an operational energy audit and current measurements in different operating ranges. To achieve this, smart meters were installed in selected energy-intensive dairy and pig farms.
As part of the project, the installed smart meter information of one of the dairy Farms is used to optimize the energy consumption of the farm and increase the degree of self-sufficiency. A good way to achieve this is by taking a closer look at the cooling process of the produced milk since it is one of the most energy consuming processes on a dairy farm. In addition an installation of an ice cooling system instead of a direct cooling system enables the possibility to store self-produced energy in the form of ice and use it later on when it is needed to cool the milk. This flattens the usual energy peaks throughout the day and increases the degree of self-sufficiency. To ensure a sufficient amount of self-produced energy with solar power plants of various sizes were designed. The different sizes of the power plants are defined by the use of the gathered smart meter data is used to cover different electric loads in addition to the ice water cooling system. Afterwards the different simulated models are compared to find the best balance between energy production, investment cost and a high degree of self-sufficiency. First results show that using an ice cooling system in combination with a solar power plant improvement the degree of self-sufficiency by up to 7.8 %.
There is a dramatic shift in credit card fraud from the offline to the online world. Large online retailers have tried to establish countermeasures and transaction data analysis technologies to lower the rate of fraudulent transactions to a manageable amount. But as retailers will always have to make a trade-off between the performance of the transaction processing, the usability of the web shop, and the overall security of it, one can assume that e-commerce fraud will still happen in the future. Thus, retailers have to collaborate with relevant business partners on the incident to find a common ground and take coordinated (legal) actions against it.
Trying to combine the information from different stakeholders will face issues due to different wordings and data formats, competing incentives of the stakeholders to participate on information sharing, as well as possible sharing restrictions that prevent them from making the information available to a larger audience. Moreover, as some of the information might be confidential or business-critical to at least one of the parties involved, a centralized system (e.g. a service in the public cloud) can not be used.
This Master Thesis is therefore analysing how far a computer supported collaborative work system based on peer-to-peer communication and Semantic Web technologies can improve the efficiency and effectivity of e-commerce fraud investigations within an inter-institutional team.
This thesis focuses on the identification of influential users, also known as key opinion leaders, within the social network Instagram. Instagram is a very popular platform to share images with the option to categorise the images by certain tags. It is possible to collect public data from Instagram via the open API of the platform.
This thesis presents a concept to create an automated crawler for this API and col- lect data into a database in order to apply algorithms from graph theory to identify opinion leaders afterwards. The sample topic for this thesis has been veganfood and all associated posts from Instagram have been crawled.
After the user data has been crawled a graph has been created to do further research with common social network analysis tools. The graph contained a total set of more than 26,000 nodes.
To identify opinion leaders from this graph, five di↵erent metrics have been applied, in particular PageRank, Betweenness centrality, Closeness Centrality, Degree and Eigen- vector centrality. After applying the di↵erent algorithms the results have been eval- uated and additionally an marketing expert with focus on social media analysed the results.
This project was able to figured out that it is possible to find opinion leaders by using the PageRank algorithm and that those opinion leaders have a very good value of en- gagement. This indicates that they show a high interaction with other users on their posts. In conclusion the additional research options are discussed to provide a future outlook.