Analyzing user feedback of on-line communities

The economic success of the World Wide Web makes it a highly competitive environment for web businesses. For this reason, it is crucial for web business owners to learn what their customers want. This thesis provides a conceptual framework and an implementation of a system that helps to better understand the behavior and potential interests of web site visitors by accounting for both explicit and implicit feedback. This thesis is divided into two parts. The first part is rooted in computer science and information systems and uses graph theory and an extended click-stream analysis to define a framework and a system tool that is useful for analyzing web user behavior by calculating the interests of the users. The second part is rooted in behavioral economics, mathematics, and psychology and is investigating influencing factors on different types of web user choices. In detail, a model for the cognitive process of rating products on the Web is defined and an importance hierarchy of the influencing factors is discovered. Both parts make use of techniques from a variety of research fields and, therefore, contribute to the area of Web Science.Welche Interessen verfolgen meine Webseiten-Nutzer? Diese Frage beschäftigt viele Betreiber von Online-Unternehmen. Um in einem solch hart umkämpften Markt wie dem des Internetbusiness erfolgreich bestehen zu können, ist es für die Entscheidungsträger dieser Unternehmen ausschlaggebend zu verstehen, welche Ziele ihre Kunden verfolgen. Hauptziel der vorliegenden Arbeit ist es, diese Frage mit Hilfe eines konzeptionellen Bezugssystems und der Implementierung eines Systems zu beantworten. Beide Elemente berücksichtigen sowohl das Verhalten, als auch das explizite und das implizite Feedback der Webseiten-Nutzer. Der vorgeschlagene Lösungsansatz unterstützt Betreiber von Online-Unternehmen dabei ihre Kunden besser zu verstehen. Dies geschieht durch das Beobachten und Auswerten des Kundenverhaltens, um daraus die vermuteten Kundeninteressen zu berechnen. Außerdem werden, um den Prozess des Feedbackgebens besser zu verstehen, diejenigen Faktoren untersucht, die die Auswahl des Webseiten-Nutzers beim Feedbackgeben beeinflussen. Folgende Forschungsfragen werden in dieser Arbeit im Hinblick auf unterschiedliche Aspekte des Feedbacks von Webseiten-Nutzern untersucht: * Was lernen wir aus der Analyse des explizit und des implizit durch die Webseiten-Nutzer ausgeführten Feedbacks? * Was sind die wichtigsten Faktoren, die das Feedback von Webseiten-Nutzern beeinflussen

Applying Machine Learning to Routine Satellite Ground Segment Operations by Means of Automated Anomaly Detection

To tackle the domain specific challenges spacecraft operations poses on anomaly detection methods, the Automated Telemetry Health Monitoring System (ATHMoS) was developed at the German Space Operations Center (GSOC) and integrated into our Visualisation and Data Analysis software (ViDA). The main challenges include the peculiarities of the telemetry data transmitted by the satellites, the limitation of resources and accuracy and usability requirements posed by the end users. The ATHMoS was designed with these challenges in mind and uses sets of generic statistical properties to derive an explainable anomaly probability

To Catch Them All: A Generic Approach for Pattern Detection in Time Series Satellite Telemetry Data

This paper presents a tool that enables automatic recognition of patterns in historical satellite telemetry datasets. This tool uses pattern-matching techniques to identify substantially similar patterns to a user selected interval within a time series. In this sense, the software serves as a powerful data exploration tool for datasets which are too large for manual inspection. The system is designed to be robust to signal types, incomplete coverage, and inconsistent sampling, which are common issues with telemetry data. In detail, three distance-based algorithms, namely, Discrete Wavelet Transform, Dynamic Time Warping, and Adaptive Piecewise Constant Approximation, two probabilistic algorithms, namely, Gaussian Mixture Model, Hidden Markov Model, as well as an ensemble approach are implemented to cover and explore a wide variety of different pattern detection techniques. The system is evaluated on real satellite data and on the UCR (University of California, Riverside) Time Series Classification Archive

Bringing a Machine Learning Based Novelty Detection Software Tool from Research to Production

This paper presents the process of bringing a machine learning based novelty detection software tool from research to production. Moreover, it sums up the necessary changes that needed to be done for developing a scientific software library into a software product with an application in space operations. This process considers the needs and expectations of all stakeholders. The system for which this process is shown is the Automated Telemetry Health Monitoring System (ATHMoS) developed at the German Space Operations Center of the German Aerospace Center. In its early phase as a research software, it paved the way for the novelty detection research. After its value for the satellite engineer’s daily work became visible, it evolved to a robust and resilient software tool that can be used in a productive environment to support the engineers in their routine work. Furthermore, the integration of the system into our Visualization and Data Analysis framework is explained. This framework has a web-based front-end for the interactive exploration and analysis of satellite telemetry data

A Modern Approach to Visualise Structured and Unstructured Space Missions' Data

In this paper the Visualisation and Data Analysis (ViDA) project, currently being developed at the German Space Operations Center (GSOC), is presented. ViDA is a modern, interactive, web-based frontend tool designed to efficiently explore various types of data generated by space missions. It is more than just a telemetry display tool and, as such, includes features from business intelligence, data science and AI tools, while being focused on the multi-spacecraft operations use case. The paper describes how the big data challenges (volume, variety, variability, complexity, value) in the context of spacecraft operations have been addressed and how the adopted solutions have been integrated into ViDA. It also highlights the importance of contextual knowledge as crucial point for the design and implementation of ViDA. The techniques used for creating appropriate visual representations of the data and their relations are described. Such visualisations are specifically designed to deliver interpretable results to the users, thus helping them to quickly extract knowledge from them during their analytical process. Finally, the integration of ViDA into the ground system and its connections to the other tools in the telemetry/telecommand chain are discussed

ARTigo – Social Image Tagging [Dataset and Images]

ARTigo is a platform that uses crowdsourcing to gather annotations (tags) on works of art (see http://www.artigo.org/). The dataset is compromised of 54.497 objects, which are associated with 18.492 artists (11.519 of which are either anonymous or unknown), 295.343 German-, French-, English-language tags, and 9.669.410 taggings. It is based on a cleansed database dump dated November 15, 2018. The cleansing concerned only the metadata of the objects; tags and taggings are provided „as is“. A current but uncleansed version of the data is available via a RESTful API at: http://www.artigo.org/api.html. The data is licensed under Creative Commons BY-NC-SA 4.0. If you are unsure whether your project is a commercial use, please contact us at: [email protected]