Daniel Zoller

Modeling and extracting load intensity profiles

Todays system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly variable load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load intensity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.

Media Bias in German Online Newspapers

Online newspapers have been established as a crucial information source, at least partially replacing traditional media like television or print media. As all other media, online newspapers are potentially affected by media bias.This describes non-neutral reporting of journalists and other news producers, e.g. with respect to specific opinions or political parties. Analysis of media bias has a long tradition in political science. However, traditional techniques rely heavily on manual annotation and are thus often limited to the analysis of small sets of articles. In this paper, we investigate a dataset that covers all political and economical news from four leading German online newspapers over a timespan of four years. In order to analyze this large document set and compare the political orientation of different newspapers, we propose a variety of automatically computable measures that can indicate media bias. As a result, statistically significant differences in the reporting about specific parties can be detected between the analyzed online newspapers.

What Users Actually Do in a Social Tagging System: A Study of User Behavior in BibSonomy

Social tagging systems have established themselves as an important part in today’s Web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. Although typical assumptions hold to a certain extent for some, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems and the way they are used on the Web. We make the dataset used in this work available to other researchers.

How social is social tagging

Social tagging systems have established themselves as an important part in todays web and have attracted the interest of our research community in a variety of investigations. This has led to several assumptions about tagging, such as that tagging systems exhibit a social component. In this work we overcome the previous absence of data for testing such an assumption. We thoroughly study social interaction, leveraging for the first time live log data gathered from the real-world public social tagging system \bibs. Our results indicate that sharing of resources constitutes an important and indeed social aspect of tagging.

On Publication Usage in a Social Bookmarking System

Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication management and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed -- so called altmetrics. In scholarly social bookmarking systems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.