area which is combining two prominent areas of computer science.
In this paper, we tackle this topic from different angles: We describe
data mining methods for ubiquitous and social data, specifically
focusing on physical and social activities, and provide exemplary
analysis results. Furthermore, we give an overview on the Ubicon
platform which provides a framework for the creation and hosting
of ubiquitous and social applications for diverse tasks and projects.
Ubicon features the collection and analysis of both physical and
social activities of users for enabling inter-connected applications
in ubiquitous and social contexts. We summarize three real-world
systems built on top of Ubicon, and exemplarily discuss the according
mining and analysis aspects. ER - TY - CONF AU - Atzmueller, Martin AU - Becker, Martin AU - Doerfel, Stephan AU - Kibanov, Mark AU - Hotho, Andreas AU - Macek, Björn-Elmar AU - Mitzlaff, Folke AU - Mueller, Juergen AU - Scholz, Christoph AU - Stumme, Gerd A2 - T1 - Ubicon: Observing Social and Physical Activities T2 - IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012 PB - IEEE CY - Washington, DC, USA PY - 2012/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2012 KW - activities KW - everyaware KW - myown KW - observing KW - physical KW - social KW - ubicon L1 - SN - N1 - N1 - AB - The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects. ER - TY - JOUR AU - Mitzlaff, Folke AU - Stumme, Gerd T1 - Relatedness of Given Names JO - Human Journal PY - 2012/ VL - 1 IS - 4 SP - 205 EP - 217 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2012relatedness.pdf M3 - KW - 2012 KW - everyaware KW - given KW - itegpub KW - l3s KW - myown KW - name KW - nameling KW - relatedness L1 - SN - N1 - N1 - AB - As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.
ER - TY - CONF AU - Schweizer, Immanuel AU - Meurisch, Christian AU - Gedeon, Julien AU - Bärtl, Roman AU - Mühlhäuser, Max A2 - T1 - Noisemap: multi-tier incentive mechanisms for participative urban sensing T2 - Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones PB - ACM CY - New York, NY, USA PY - 2012/ M2 - VL - IS - SP - 9:1 EP - 9:5 UR - http://doi.acm.org/10.1145/2389148.2389157 M3 - 10.1145/2389148.2389157 KW - everyaware KW - noise KW - noisemap KW - sensing KW - urban L1 - SN - 978-1-4503-1778-8 N1 - Noisemap N1 - AB - Noise pollution is a problem increasingly acknowledged by authorities and governments around the globe. At last year's PhoneSense we presented Noisemap, a participating sensing application to accurately measure noise pollution. Noisemap incorporated frequency calibration to overcome the limited microphone hardware. The challenge remaining is how to motivate smartphone users to sacrifice their time and battery on measuring noise. A user study was conducted with 49 users divided into three groups. As expected the average measurements taken per user increased from 402 to 3,357 as the number of incentive schemes increased. Over the course of 7 weeks the users captured more than 85, 000 measurements, measuring for more than six hours on average. ER - TY - THES AU - Flohr, Oliver T1 - Extraktion und Visualisierung ortsbezogener Informationen mit Tag-Clouds PY - 2011/08 PB - Gottfried Wilhelm Leibniz Universität Hannover SP - EP - UR - http://www.se.uni-hannover.de/pub/File/pdfpapers/Flohr2011a.pdf M3 - KW - bookmarking KW - cloud KW - collaborative KW - everyaware KW - geo KW - location KW - social KW - tagging KW - visualization L1 - N1 - N1 - AB - Informationen so aufzubereiten, dass sie für eine bestimmte Situation nützlich sind, ist eine große Herausforderung. In solchen Situationen soll ein Benutzer, wenn er sich an einem fremden Ort befindet, mit Hilfe des Android Smartphone interessante und wis- senswerte Informationen anzeigen lassen. Um dies bewerkstelligen zu können, muss es eine georeferenzierte Informationsquelle geben. Außerdem muss ein Konzept vor- handen sein, um diese Daten zu sammeln und so aufzubereiten, dass der Benutzer diese auch nützlich findet. Es muss eine Visualisierung dieser Daten geben, da der Platz zur Anzeige auf Smartphones sehr begrenzt ist. Als georeferenzierte Informationsquelle wird die Online-Enzyklopädie Wikipedia ge- nutzt, diese ist frei zugänglich und auch sehr umfassend. In dieser Arbeit wird das Konzept zur Sammlung und Aufbereitung von relevanten Daten behandelt. Zur In- formationsvisualisierung wird die Methode der Schlagwortwolke (engl. Tag-Cloud) verwendet. It is a major challenge to prepare useful information for a particular situation. In this situation an Android smartphone user wants to display interesting and important facts about an unknown place. To manage this task existence of a geo-referenced source of information has to be ensured. In order to collect and prepare this data a creation of concept is needed. Due to limited display space, it is necessary to construct a suitable visualization of this data. Wikipedia is used as a geo-referenced information resource, because it has open-access and it offers global geo-referenced information. This thesis covers the concept of col- lecting and preparing relevant data. To visualize information a tag cloud is used. ER - TY - JOUR AU - Ganti, R.K. AU - Ye, Fan AU - Lei, Hui T1 - Mobile crowdsensing: current state and future challenges JO - Communications Magazine, IEEE PY - 2011/ VL - 49 IS - 11 SP - 32 EP - 39 UR - http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6069707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6069707 M3 - 10.1109/MCOM.2011.6069707 KW - crowdsensing KW - everyaware KW - mobile KW - participatory KW - sensing KW - sitc L1 - SN - N1 - IEEE Xplore - Mobile crowdsensing: current state and future challenges N1 - AB - An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy. ER - TY - JOUR AU - Harrison, Jill Lindsey T1 - Parsing “Participation” in Action Research: Navigating the Challenges of Lay Involvement in Technically Complex Participatory Science Projects JO - Society & Natural Resources: An International Journal PY - 2011/ VL - 24 IS - SP - 702 EP - 716 UR - http://www.informaworld.com/10.1080/08941920903403115 M3 - KW - everyaware KW - participatory KW - science KW - toread L1 - SN - N1 - Parsing Participation in Action Research: Navigating the Challenges of Lay Involvement in Technically Complex Participatory Science Projects - Society & Natural Resources: An International Journal N1 - AB - ER - TY - JOUR AU - Kamel Boulos, M N AU - Resch, B AU - Crowley, D N AU - Breslin, J G AU - Sohn, G AU - Burtner, R AU - Pike, W A AU - Jezierski, E AU - Chuang, K Y T1 - Crowdsourcing, citizen sensing and Sensor Web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples JO - Int J Health Geogr PY - 2011/12 VL - 10 IS - 1 SP - 67 EP - 67 UR - http://www.ncbi.nlm.nih.gov/pubmed/22188675 M3 - 10.1186/1476-072X-10-67 KW - crowdsource KW - everyaware KW - sensing KW - toread L1 - SN - N1 - Crowdsourcing, citizen sensing and Sensor... [Int J Health Geogr. 2011] - PubMed - NCBI N1 - AB - ABSTRACT: 'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world. ER - TY - CONF AU - A2 - Second International Workshop on Sensing Applications on Mobile Phones, ACM SenSys 2011 T1 - NoiseMap - Real-time participatory noise maps T2 - PB - CY - PY - 2011/ M2 - VL - IS - SP - EP - UR - http://research.microsoft.com/en-us/um/redmond/events/phonesense2011/papers/NoiseMap.pdf M3 - KW - everyaware KW - noise KW - sensing KW - toread KW - urban L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Fuchs, Erich AU - Gruber, Thiemo AU - Pree, Helmuth AU - Sick, Bernhard T1 - Temporal data mining using shape space representations of time series JO - Neurocomputing PY - 2010/ VL - 74 IS - 1–3 SP - 379 EP - 393 UR - http://www.sciencedirect.com/science/article/pii/S0925231210002237 M3 - 10.1016/j.neucom.2010.03.022 KW - data KW - everyaware KW - mining KW - orthogonal KW - polynom KW - polynoms KW - representations KW - series KW - shape KW - space KW - temoral KW - time L1 - SN - N1 - ScienceDirect.com - Neurocomputing - Temporal data mining using shape space representations of time series N1 - AB - Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining. ER - TY - CONF AU - Rezel, R. AU - Liang, S. A2 - T1 - SWE-FE: Extending folksonomies to the Sensor Web T2 - 2010 International Symposium on Collaborative Technologies and Systems (CTS) PB - IEEE CY - PY - 2010/05 M2 - VL - IS - SP - 349 EP - 356 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494 M3 - 10.1109/CTS.2010.5478494 KW - collaborative KW - everyaware KW - folksonomy KW - sensor KW - tagging KW - taggingsurvey KW - toread L1 - SN - N1 - N1 - AB - This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment. ER - TY - JOUR AU - Song, Chaoming AU - Qu, Zehui AU - Blumm, Nicholas AU - Barabási, Albert-László T1 - Limits of Predictability in Human Mobility JO - Science PY - 2010/ VL - 327 IS - 5968 SP - 1018 EP - 1021 UR - http://www.sciencemag.org/content/327/5968/1018.abstract M3 - 10.1126/science.1177170 KW - dm KW - everyaware KW - position KW - prediction KW - toread KW - vorhersage L1 - SN - N1 - N1 - AB - A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual’s trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis. ER - TY - CONF AU - Yassine, Mohamed AU - Hajj, Hazem A2 - Fan, Wei A2 - Hsu, Wynne A2 - Webb, Geoffrey I. A2 - Liu, Bing A2 - Zhang, Chengqi A2 - Gunopulos, Dimitrios A2 - Wu, Xindong T1 - A Framework for Emotion Mining from Text in Online Social Networks. T2 - ICDM Workshops PB - IEEE Computer Society CY - PY - 2010/ M2 - VL - IS - SP - 1136 EP - 1142 UR - http://dblp.uni-trier.de/db/conf/icdm/icdmw2010.html#YassineH10 M3 - KW - emotion KW - everyaware KW - mining KW - text KW - toread L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Vinciarelli, Alessandro AU - Pantic, Maja AU - Bourlard, Hervé T1 - Social signal processing: Survey of an emerging domain JO - Image and Vision Computing PY - 2009/november VL - 27 IS - 12 SP - 1743 EP - 1759 UR - http://dx.doi.org/10.1016/j.imavis.2008.11.007 M3 - 10.1016/j.imavis.2008.11.007 KW - everyaware KW - introduction KW - signal KW - social KW - survey L1 - SN - N1 - CiteULike: Social signal processing: Survey of an emerging domain N1 - AB - The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing. ER - TY - JOUR AU - Juhos, István AU - Makra, László AU - Tóth, Balázs T1 - Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis JO - Simulation Modelling Practice and Theory PY - 2008/ VL - 16 IS - 9 SP - 1488 EP - 1502 UR - http://www.sciencedirect.com/science/article/pii/S1569190X08001585 M3 - 10.1016/j.simpat.2008.08.006 KW - everyaware KW - model KW - svm KW - toread L1 - SN - N1 - ScienceDirect.com - Simulation Modelling Practice and Theory - Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis N1 - AB - The main aim of this paper is to predict NO and NO2 concentrations four days in advance comparing two artificial intelligence learning methods, namely, Multi-Layer Perceptron and Support Vector Machines on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged in order to build a model for predicting NO and NO2 concentrations several hours in advance. The prediction of NO and NO2 concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO2. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, Multi-Layer Perceptron and Support Vector Regression are used to provide efficient non-linear models for NO and NO2 times series predictions. Multi-Layer Perceptron is widely used to predict these time series, but Support Vector Regression has not yet been applied for predicting NO and NO2 concentrations. Grid search is applied to select the best parameters for the learners. To get rid of the curse of dimensionality of the spatial embedding of the time series Principal Component Analysis is taken to reduce the dimension of the embedded data. Three commonly used linear algorithms were considered as references: one-day persistence, average of several-day persistence and linear regression. Based on the good results of the average of several-day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO2 the improvement of the prediction is considerable; however, it is less notable than for NO. ER - TY - JOUR AU - Li, Chunming AU - Wei, Dong AU - Vause, Jonathan AU - Liu, Jianping T1 - Towards a societal scale environmental sensing network with public participation JO - International Journal of Sustainable Development & World Ecology PY - 0/ VL - 0 IS - 0 SP - 1 EP - 6 UR - http://www.tandfonline.com/doi/abs/10.1080/13504509.2013.779326 M3 - 10.1080/13504509.2013.779326 KW - everyaware KW - noise KW - toread L1 - SN - N1 - Taylor & Francis Online :: Towards a societal scale environmental sensing network with public participation - International Journal of Sustainable Development & World Ecology - N1 - AB - ER -