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This market research paper has been prepared under the supervision of Prof. Dr. Wolfgang Veit of TH Köln and Prof. Dr. Carol Scovotti of University of Wisconsin-Whitewater in the course of the inter-university cross-border collaboration student research project “Export Opportunity Surveys (EOS)”. This study explores organic coconut oil export opportunities to the German and US markets.
This market research paper has been prepared under the supervision of Prof. Dr. Wolfgang Veit of TH Köln and Prof. Dr. Carol Scovotti of University of Wisconsin-Whitewater in the course of the inter-university cross-border collaboration student research project “Export Opportunity Surveys (EOS)”. This study explores organic canned tomatoes export opportunities to the German and US markets.
The topic for the thesis originated from the CAP4ACCESS project run by the European Commission and its partners, which deals towards the sensiti-zation of people and development of tools for awareness about people with movement disabilities. The explorative analysis is never ending and to explore and find interest-ing patterns and the results is a tedious task. Therefore, a scientific approach was very important. To start with, familiarizing the domain and the data sources were done. Thereafter, selection of methodology for data analysis was done which resulted in the use of CRISP-DM methodology. The data sources are the source of blood to the analysis methodology, and as there were two sources of data that is MICROM and OSM Wheelchair History(OWH), it was important to integrate them together to extract relevant datasets. Therefore a functional and technically impure data warehouse was created, from which the datasets are extracted and analysed.The next task was to select appropriate tools for analysis. This task was very important as the data set although was not big data but con-tained a large number of rows. After careful analysis, Apache spark and its machine learning library were utilized for building and testing supervised models. DataFrame API for Python, Pandas, the machine learning library Sci-kit learn provided unsupervised algorithms for analysis, the association rule analysis was performed using WEKA. Tableau[21] and Matplotlib[24] provide attractive visualizations for representation and analysis.
An empirical evaluation of using the Swift language as the underlying technology of RESTful APIs
(2016)
The purpose of the current thesis is to determine the appropriateness of using the Swift language as the underlying technology for the development of RESTful APIs in a Linux environment. The current paper describes the process of designing, implementing and testing individual RESTful API components based on Node.js, PHP, Python and Swift and seeks to determine whether Swift is a viable alternative.
The thesis begins by defining a methodology for implementing and testing individual RESTful API components based on Node.js, PHP, Python and Swift. It then proceeds to detail the implementation and testing processes, following with an analytic discussion regarding the advantages and drawbacks of using the Swift language as the underlying technology for RESTful APIs and server-side Linux-based applications in general.
Based on the implementation process and on the results of the previously mentioned evaluation phase, it can be stated that the Swift language is not yet ready to be used in a production environment. However, its rapid evolution and potential for surpassing its competitors in the foreseeable future make it an ideal candidate for implementing RESTful APIs to be used in development environments.
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.