@inproceedings{dallmann2015media, author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas}, title = {Media Bias in German Online Newspapers}, booktitle = {26th ACM Conference on Hypertext and Social Media}, publisher = {ACM}, address = {Cyprus, Turkey, September 1-4}, year = {2015}, keywords = {2015, bias, german, myown, newspaper} }

%0 = inproceedings %A = Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas %B = 26th ACM Conference on Hypertext and Social Media %C = Cyprus, Turkey, September 1-4 %D = 2015 %I = ACM %T = Media Bias in German Online Newspapers

@inproceedings{ring2015condist, author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas}, title = {ConDist: A Context-Driven Categorical Distance Measure}, editor = {ECMLPKDD2015}, year = {2015}, keywords = {2015, categorical, data, learning, measure, myown, similarity, unsupervised} }

%0 = inproceedings %A = Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas %D = 2015 %T = ConDist: A Context-Driven Categorical Distance Measure

@inproceedings{singer2015hyptrails, author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.}, title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails}, booktitle = {24th International World Wide Web Conference (WWW2015)}, publisher = {ACM}, address = {Firenze, Italy}, year = {2015}, url = {http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf}, keywords = {2015, bibsonomy, compare, human, hypotheses, hyptrails, myown, trails, web} }

%0 = inproceedings %A = Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M. %B = 24th International World Wide Web Conference (WWW2015) %C = Firenze, Italy %D = 2015 %I = ACM %T = Hyptrails: A bayesian approach for comparing hypotheses about human trails %U = http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf

Today’s 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 vari- able 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 inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time.
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.

@inproceedings{vkistowski2015modeling, author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas}, title = {Modeling and Extracting Load Intensity Profiles}, booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, year = {2015}, keywords = {2015, extracting, intensity, load, modeling, myown, profile}, abstract = {Today’s 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 vari- able 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 inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time.
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.} }

%0 = inproceedings %A = v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas %B = Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) %D = 2015 %T = Modeling and Extracting Load Intensity Profiles

Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement 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 sys- tems, 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.

@inproceedings{zoller2015publication, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, title = {On Publication Usage in a Social Bookmarking System}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, year = {2015}, keywords = {2015, altmetrics, bookmarking, impact, myown, publication, social, usage}, abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement 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 sys- tems, 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.} }

%0 = inproceedings %A = Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas %B = Proceedings of the 2015 ACM Conference on Web Science %D = 2015 %T = On Publication Usage in a Social Bookmarking System

Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context.
this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions.

@misc{becker2014subjective, author = {Becker, Martin and Hotho, Andreas and Mueller, Juergen and Kibanov, Mark and Atzmueller, Martin and Stumme, Gerd}, title = {Subjective vs. Objective Data: Bridging the Gap}, year = {2014}, url = {http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/}, keywords = {2015, everyaware, myown}, abstract = {Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context. In this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions. } }

%0 = misc %A = Becker, Martin and Hotho, Andreas and Mueller, Juergen and Kibanov, Mark and Atzmueller, Martin and Stumme, Gerd %B = } %C = %D = 2014 %I = %T = Subjective vs. Objective Data: Bridging the Gap} %U = http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/