Wissenschaftliche Arbeiten

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Abschlussarbeiten

2018

Schlacher Jan Peter

Neo4-js: Object-Graph Mapping with Typed JavaScript and Neo4j

Bakk

Bakk
2018

Friedrich Matthias

Businesssuite, an Affordable and Secure Toolsuite for Managing Customer Data and Invoices

Bakk

Bakk
2018

Schaffer Robert

Evaluation of Vote/Veto Classifier i

Bakk

Bakk
Authorship identification techniques are used to determine whether a document or text was written by a specific author or not. This includes discovering the rightful author from a finite list of authors for a previously unseen text or to verify if a text was written by a specific author. As digital media continues to get more important every day these techniques need to be also applied to shorter texts like emails, newsgroup posts, social media entries, forum posts and other forms of text. Especially because of the anonymity of the Internet this has become an important task. The existing Vote/Veto framework evaluated in this thesis is a system for authorship identification. The evaluation covers experiments to find reasonable settings for the framework and of course all tests to determine the accuracy and runtime of it. The same tests for accuracy and runtime have been carried out by a number of inbuilt classifiers of the existing software Weka to compare the results. All results have been written to tables and were compared to each other. In terms of accuracy Vote/Veto mostly delivered better results than Weka’s inbuilt classifiers even though the runtime was longer and more memory was necessary. Some settings provided good accuracy results with reasonable runtimes.
2018

Leitgeb Martin

GPS Car Insurance: Assumptions versus Facts

Bakk

Bakk
2018

Bruchmann Andreas

Privacy Protection via Pseudo Relevance Feedback

Bakk

Bakk
2018

Resch Sebastian

Implementation and Evaluation of a Bookmark and History Content Search Browser Add-on

Bakk

Bakk
2018

Lackner Patrick

Computing Cluster for Big Data Analysis

Bakk

Bakk
2018

Anthofer Daniel

A Neural Network for Open Information Extraction from German Text i

Master

Master
Systems that extract information from natural language texts usually need to consider language-dependent aspects like vocabulary and grammar. Compared to the development of individual systems for different languages, development of multilingual information extraction (IE) systems has the potential to reduce cost and effort. One path towards IE from different languages is to port an IE system from one language to another. PropsDE is an open IE (OIE) system that has been ported from the English system PropS to the German language. There are only few OIE methods for German available. Our goal is to develop a neural network that mimics the rules of an existing rule-based OIE system. For that, we need to learn about OIE from German text. By performing an analysis and a comparison of the rule-based systems PropS and PropsDE, we can observe a step towards multilinguality, and we learn about German OIE. Then we present a deep-learning based OIE system for German, which mimics the behaviour of PropsDE. The precision in directly imitating PropsDE is 28.1%. Our model produces many extractions that appear promising, but are not fully correct.
2018

Schiestl Andreas

Businesssuite, an Affordable and Secure Toolsuite for Managing Customer Data and Invoices

Bakk

Bakk
2018

Leitner Lorenz

Implementation and Evaluation of a Bookmark and History Content Search Browser Add-on

Bakk

Bakk
2018

Polz Hans Georg

Is Google’s Wisdom-Of-The-Crowd a Valid Approach to Discerning Truth in the Age of Fake News

Bakk

Bakk
2018

Fernández Alonso, Miguel Yuste

Mining Frequent Patterns in Environmental Sensor Data i

Bakk

Bakk
The advances in data science provide us with a vast array of tools to analyse and better understand our environment. Of special interest to us is the topic of sequential pattern mining, in which statistic patterns are found within sequences of discrete data. In this work, we review some of the major techniques currently offered by the pattern mining field. We also develop a proof of concept tool for frequent itemset mining in Tinkerforge sensor data, showing how the application of the FP-Growth algorithm to Tinkerforge sensor data can provide valuable observations and offer an inexpensive yet powerful setting for further knowledge discovery processes. Lastly, we discuss some of the possible future lines of development of the presented problem.
Kontakt Karriere

Hiermit erkläre ich ausdrücklich meine Einwilligung zum Einsatz und zur Speicherung von Cookies. Weiter Informationen finden sich unter Datenschutzerklärung

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.

Close