PUMA publications for /tag/2015https://puma.uni-kassel.de/tag/2015PUMA RSS feed for /tag/20152024-03-29T05:41:08+01:00Subjective vs. Objective Data: Bridging the Gaphttps://puma.uni-kassel.de/bibtex/233cf40cc46170f51767c46d2ec14a495/hothohotho2015-07-24T18:14:51+02:002015 everyaware myown <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Becker" itemprop="url" href="/author/Martin%20Becker"><span itemprop="name">M. Becker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Juergen Mueller" itemprop="url" href="/author/Juergen%20Mueller"><span itemprop="name">J. Mueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mark Kibanov" itemprop="url" href="/author/Mark%20Kibanov"><span itemprop="name">M. Kibanov</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><em>CSSWS 2014, Poster, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Fri Jul 24 18:14:51 CEST 2015CSSWS 2014, PosterSubjective vs. Objective Data: Bridging the Gap20142015 everyaware myown 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. On Publication Usage in a Social Bookmarking Systemhttps://puma.uni-kassel.de/bibtex/2548a7010ee2726f28e04e5c6e5fd6e2d/hothohotho2015-07-24T18:10:28+02:002015 altmetrics bookmarking impact myown publication social usage <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Zoller" itemprop="url" href="/author/Daniel%20Zoller"><span itemprop="name">D. Zoller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2015 ACM Conference on Web Science</span>, </em></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Fri Jul 24 18:10:28 CEST 2015Proceedings of the 2015 ACM Conference on Web ScienceOn Publication Usage in a Social Bookmarking System20152015 altmetrics bookmarking impact myown publication social usage 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.Modeling and Extracting Load Intensity Profileshttps://puma.uni-kassel.de/bibtex/2f449d3cf35941636f96d72aaf620a275/hothohotho2015-07-24T18:13:03+02:002015 extracting intensity load modeling myown profile <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jóakim v. Kistowski" itemprop="url" href="/author/J%c3%b3akim%20v.%20Kistowski"><span itemprop="name">J. v. Kistowski</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Herbst. Nikolas" itemprop="url" href="/author/Herbst.%20Nikolas"><span itemprop="name">H. Nikolas</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Zoller" itemprop="url" href="/author/Daniel%20Zoller"><span itemprop="name">D. Zoller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Samuel Kounev" itemprop="url" href="/author/Samuel%20Kounev"><span itemprop="name">S. Kounev</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)</span>, </em></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Fri Jul 24 18:13:03 CEST 2015Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)Modeling and Extracting Load Intensity Profiles20152015 extracting intensity load modeling myown profile 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.
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 Newspapershttps://puma.uni-kassel.de/bibtex/2addfd0d84b4347392dc94a4bec400412/hothohotho2015-07-24T17:59:49+02:002015 bias german myown newspaper <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Dallmann" itemprop="url" href="/author/Alexander%20Dallmann"><span itemprop="name">A. Dallmann</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Florian Lemmerich" itemprop="url" href="/author/Florian%20Lemmerich"><span itemprop="name">F. Lemmerich</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Zoller" itemprop="url" href="/author/Daniel%20Zoller"><span itemprop="name">D. Zoller</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">26th ACM Conference on Hypertext and Social Media</span>, </em></span><em>Cyprus, Turkey, September 1-4, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Fri Jul 24 17:59:49 CEST 2015Cyprus, Turkey, September 1-426th ACM Conference on Hypertext and Social MediaMedia Bias in German Online Newspapers20152015 bias german myown newspaper Hyptrails: A bayesian approach for comparing hypotheses about human trailshttps://puma.uni-kassel.de/bibtex/25d21e53dc91b35a4a6cb6b9ec858045d/hothohotho2015-05-27T15:20:47+02:002015 bibsonomy compare human hypotheses hyptrails myown trails web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Singer" itemprop="url" href="/author/P.%20Singer"><span itemprop="name">P. Singer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="D. Helic" itemprop="url" href="/author/D.%20Helic"><span itemprop="name">D. Helic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Strohmaier" itemprop="url" href="/author/M.%20Strohmaier"><span itemprop="name">M. Strohmaier</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">24th International World Wide Web Conference (WWW2015)</span>, </em></span><em>Firenze, Italy, </em><em>ACM, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>Mai 2015<meta content="Mai 2015" itemprop="datePublished"/></span></em>)Wed May 27 15:20:47 CEST 2015Firenze, Italy24th International World Wide Web Conference (WWW2015)May 18 - May 22Hyptrails: A bayesian approach for comparing hypotheses about human trails20152015 bibsonomy compare human hypotheses hyptrails myown trails web ConDist: A Context-Driven Categorical Distance Measurehttps://puma.uni-kassel.de/bibtex/2a2f9d649f2856677e4d886a3b517404d/hothohotho2015-07-24T18:03:08+02:002015 categorical data learning measure myown similarity unsupervised <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Ring" itemprop="url" href="/author/Markus%20Ring"><span itemprop="name">M. Ring</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Florian Otto" itemprop="url" href="/author/Florian%20Otto"><span itemprop="name">F. Otto</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Becker" itemprop="url" href="/author/Martin%20Becker"><span itemprop="name">M. Becker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Niebler" itemprop="url" href="/author/Thomas%20Niebler"><span itemprop="name">T. Niebler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dieter Landes" itemprop="url" href="/author/Dieter%20Landes"><span itemprop="name">D. Landes</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Fri Jul 24 18:03:08 CEST 2015ConDist: A Context-Driven Categorical Distance Measure20152015 categorical data learning measure myown similarity unsupervised