@article{10.1371/journal.pone.0136763, abstract = {

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

}, author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, doi = {10.1371/journal.pone.0136763}, interhash = {6abb09b5ac2137e557a84d7be10009b4}, intrahash = {f35761dd0fbd9ad8af7c8099e0b6aac4}, journal = {PLoS ONE}, month = {08}, number = 8, pages = {e0136763}, publisher = {Public Library of Science}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0136763}, volume = 10, year = 2015 } @article{kluegl2013exploiting, abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, interhash = {9ef3f543e4cc9e2b0ef078595f92013b}, intrahash = {fbaab25e96dd20d96ece9d7fefdc3b4f}, journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics}, pages = {111-125}, title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields}, volume = 30, year = 2013 } @misc{becker2014subjective, 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. }, author = {Becker, Martin and Hotho, Andreas and Mueller, Juergen and Kibanov, Mark and Atzmueller, Martin and Stumme, Gerd}, howpublished = {CSSWS 2014, Poster}, interhash = {615afda9869c5e0facc8bdb5534760aa}, intrahash = {33cf40cc46170f51767c46d2ec14a495}, title = {Subjective vs. Objective Data: Bridging the Gap}, url = {http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/}, year = 2014 } @inproceedings{vkistowski2015modeling, 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. 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.}, author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas}, booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, interhash = {9f0be929d7bcc057c778f6b44e73cf4c}, intrahash = {f449d3cf35941636f96d72aaf620a275}, title = {Modeling and Extracting Load Intensity Profiles}, year = 2015 } @inproceedings{zoller2015publication, 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.}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, interhash = {3515b34cd19959cee5fafbf4467a75ed}, intrahash = {548a7010ee2726f28e04e5c6e5fd6e2d}, title = {On Publication Usage in a Social Bookmarking System}, year = 2015 } @incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, interhash = {3a55606e91328ca0191127b1fafe189e}, intrahash = {84d9498b73de976d8d550c6761d4be0d}, pages = {542--547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 } @inproceedings{ring2015condist, author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas}, editor = {ECMLPKDD2015}, interhash = {c062a57a17a0910d6c27ecd664502ac1}, intrahash = {a2f9d649f2856677e4d886a3b517404d}, title = {ConDist: A Context-Driven Categorical Distance Measure}, year = 2015 } @inproceedings{dallmann2015media, address = {Cyprus, Turkey, September 1-4}, author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas}, booktitle = {26th ACM Conference on Hypertext and Social Media}, interhash = {6b2daa7830c5e504543dcdaefed46285}, intrahash = {addfd0d84b4347392dc94a4bec400412}, publisher = {ACM}, title = {Media Bias in German Online Newspapers}, year = 2015 } @inproceedings{singer2015hyptrails, address = {Firenze, Italy}, author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.}, booktitle = {24th International World Wide Web Conference (WWW2015)}, interhash = {d33e150aa37dcd618388960286f8a46a}, intrahash = {5d21e53dc91b35a4a6cb6b9ec858045d}, month = {May 18 - May 22}, organization = {ACM}, publisher = {ACM}, title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails}, url = {http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf}, year = 2015 } @article{noKey, abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the }, author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, doi = {10.1007/s13278-014-0216-2}, eid = {216}, interhash = {7e02f08a123c801c33ac93109394adfb}, intrahash = {5b268a7c5308af783c3028573ffcd0c0}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, language = {English}, number = 1, publisher = {Springer Vienna}, title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks}, url = {http://dx.doi.org/10.1007/s13278-014-0216-2}, volume = 4, year = 2014 } @misc{singer2014hyptrails, abstract = {When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.}, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, interhash = {54535487cdfa9024073c07e336e03d70}, intrahash = {07a19041ef1bfd5cef707e03d1510d5e}, note = {cite arxiv:1411.2844}, title = {HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web}, url = {http://arxiv.org/abs/1411.2844}, year = 2014 } @article{cimiano05learning, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, ee = {http://www.jair.org/papers/paper1648.html}, interhash = {4c09568cff62babd362aab03095f4589}, intrahash = {eaaf0e4b3a8b29fab23b6c15ce2d308d}, journal = {Journal on Artificial Intelligence Research}, pages = {305-339}, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, url = {http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05}, volume = 24, year = 2005 } @proceedings{jannach2014proceedings, bibsource = {dblp computer science bibliography, http://dblp.org}, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, interhash = {a1a704ec9c98e6031a1444c6eccc7c0a}, intrahash = {09cb7c63e60bd3c5e6773c9c871a8aba}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, url = {http://ceur-ws.org/Vol-1271}, volume = 1271, year = 2014 } @proceedings{cellier2014proceedings, bibsource = {dblp computer science bibliography, http://dblp.org}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie{-}Francine and Toussaint, Yannick}, interhash = {212d282598a034c37510c1c08c4f3a34}, intrahash = {cfb7265080d484cfda32e1fbdaff361f}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 1st International Workshop on Interactions between Data Mining and Natural Language Processing co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September 15, 2014}, url = {http://ceur-ws.org/Vol-1202}, volume = 1202, year = 2014 } @inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014}, doi = {10.1145/2645710.2645786}, interhash = {b465a3695da123d6ee9de1675cb3d480}, intrahash = {5773f799bec72240eda5e6cfb6a03d7b}, pages = 395, title = {The sixth {ACM} RecSys workshop on recommender systems and the social web}, url = {http://doi.acm.org/10.1145/2645710.2645786}, year = 2014 } @inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, interhash = {9223d6d728612c8c05a80b5edceeb78b}, intrahash = {11fab5468dd4b4e3db662ea5e68df8e0}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 } @inproceedings{doerfel2014evaluating, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Proceedings of the 16th {LWA} Workshops: KDML, {IR} and FGWM, Aachen, Germany, September 8-10, 2014.}, editor = {Seidl, Thomas and Hassani, Marwan and Beecks, Christian}, interhash = {955cd7c6f7652b7c531b699464925b1f}, intrahash = {4b2e73c82b5a84e1959ad66aaad4a235}, pages = {18--19}, publisher = {CEUR-WS.org}, title = {Evaluating Assumptions about Social Tagging - {A} Study of User Behavior in BibSonomy}, url = {http://ceur-ws.org/Vol-1226/paper06.pdf}, year = 2014 } @article{atzmueller2014ubicon, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.}, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, doi = {10.1080/13614568.2013.873488}, interhash = {6364e034fa868644b30618dc887c0270}, intrahash = {176e4f2816af5fe1630ed65e062900ce}, journal = {New Review of Hypermedia and Multimedia}, number = 1, pages = {53--77}, title = {{Ubicon and its Applications for Ubiquitous Social Computing}}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488}, volume = 20, year = 2014 } @article{singer2013computing, abstract = {In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.}, author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, doi = {10.4018/ijswis.2013100103}, interhash = {3377abe1838bd1f650b317ed1fca4dfe}, intrahash = {5262c48a2e2791d28610712e3bf5cf55}, issn = {15526283}, journal = {International Journal on Semantic Web and Information Systems (IJSWIS)}, number = 4, pages = {41--70}, publisher = {IGI Global}, refid = {102707}, title = {Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia}, url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijswis.2013100103}, volume = 9, year = 2013 } @misc{doerfel2014course, abstract = {Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, interhash = {65f287480af20fc407f7d26677f17b72}, intrahash = {988ea3a9b85ec0656e27750e4080325c}, note = {cite arxiv:1401.0629}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, url = {http://arxiv.org/abs/1401.0629}, year = 2014 }