@article{6069707,
  abstract = {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.},
  author = {Ganti, R.K. and Ye, Fan and Lei, Hui},
  doi = {10.1109/MCOM.2011.6069707},
  interhash = {8e5a7301965e4703f5caaf45f7a823d4},
  intrahash = {44321e103e910e997453ffcb9877ac73},
  issn = {0163-6804},
  journal = {Communications Magazine, IEEE},
  number = 11,
  pages = {32-39},
  title = {Mobile crowdsensing: current state and future challenges},
  url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6069707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6069707},
  volume = 49,
  year = 2011
}

@inproceedings{Schweizer:2012:NMI:2389148.2389157,
  abstract = {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.},
  acmid = {2389157},
  address = {New York, NY, USA},
  articleno = {9},
  author = {Schweizer, Immanuel and Meurisch, Christian and Gedeon, Julien and B\"{a}rtl, Roman and M\"{u}hlh\"{a}user, Max},
  booktitle = {Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones},
  doi = {10.1145/2389148.2389157},
  interhash = {2115b4afb0ea994189332bfd209fbee7},
  intrahash = {4d5bc70c5ee9c8250f6e4e6b4e4d8518},
  isbn = {978-1-4503-1778-8},
  location = {Toronto, Ontario, Canada},
  numpages = {5},
  pages = {9:1--9:5},
  publisher = {ACM},
  series = {PhoneSense '12},
  title = {Noisemap: multi-tier incentive mechanisms for participative urban sensing},
  url = {http://doi.acm.org/10.1145/2389148.2389157},
  year = 2012
}

@inproceedings{noauthororeditor2011noisemap,
  editor = {Second International Workshop on Sensing Applications on Mobile Phones, ACM SenSys 2011},
  interhash = {fb255bb53f64fc403f4e03cb73577bb5},
  intrahash = {248ff44fcf7b397c118a33116d05326b},
  title = {NoiseMap - Real-time participatory noise maps},
  url = {http://research.microsoft.com/en-us/um/redmond/events/phonesense2011/papers/NoiseMap.pdf},
  year = 2011
}

@inproceedings{mueller-2013a,
  abstract = {An increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data enabling the Internet of Things. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common feed-centric approach limit the flexibility of these platforms. We aim at providing a concept that supports data ranging from text-based formats like JSON to images and video footage. Furthermore, we introduce the concept of extensions, which allows to enrich existing data points with additional information, thus, taking a data point centric approach. This enables us to gain semantic and user specific context by attaching subjective data to objective values. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.},
  author = {Becker, Martin and Mueller, Juergen and Hotho, Andreas and Stumme, Gerd},
  booktitle = {1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, Switzerland -- September 9, 2013. Proceedings},
  interhash = {5302866e7849d40a44deab166b6c4d36},
  intrahash = {35eef1ecdac9d83d3bfbcac22c31984a},
  note = {Accepted for publication},
  pages = {New York, NY, USA},
  publisher = {ACM},
  title = {A Generic Platform for Ubiquitous and Subjective Data},
  year = 2013
}

@misc{haslhofer2013semantic,
  abstract = {Tags assigned by users to shared content can be ambiguous. As a possible solution, we propose semantic tagging as a collaborative process in which a user selects and associates Web resources drawn from a knowledge context. We applied this general technique in the specific context of online historical maps and allowed users to annotate and tag them. To study the effects of semantic tagging on tag production, the types and categories of obtained tags, and user task load, we conducted an in-lab within-subject experiment with 24 participants who annotated and tagged two distinct maps. We found that the semantic tagging implementation does not affect these parameters, while providing tagging relationships to well-defined concept definitions. Compared to label-based tagging, our technique also gathers positive and negative tagging relationships. We believe that our findings carry implications for designers who want to adopt semantic tagging in other contexts and systems on the Web.},
  author = {Haslhofer, Bernhard and Robitza, Werner and Lagoze, Carl and Guimbretiere, Francois},
  interhash = {84516aa456894b6d6adf86abd2386656},
  intrahash = {a653f1a0a1ac5084e80757ec277b1184},
  note = {cite arxiv:1304.1636Comment: 10 pages},
  title = {Semantic Tagging on Historical Maps},
  url = {http://arxiv.org/abs/1304.1636},
  year = 2013
}

@article{doi:10.1080/13504509.2013.779326,
  author = {Li, Chunming and Wei, Dong and Vause, Jonathan and Liu, Jianping},
  doi = {10.1080/13504509.2013.779326},
  eprint = {http://www.tandfonline.com/doi/pdf/10.1080/13504509.2013.779326},
  interhash = {397604f8584c402f7c14dc2d2935baaa},
  intrahash = {a5a19958a5f397b467abdabe2f8adf69},
  journal = {International Journal of Sustainable Development & World Ecology},
  number = 0,
  pages = {1-6},
  title = {Towards a societal scale environmental sensing network with public participation},
  url = {http://www.tandfonline.com/doi/abs/10.1080/13504509.2013.779326},
  volume = 0,
  year = 0
}

@article{Juhos20081488,
  abstract = {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.},
  author = {Juhos, István and Makra, László and Tóth, Balázs},
  doi = {10.1016/j.simpat.2008.08.006},
  interhash = {b8e240cb2c8bb4d0f42aeda944a3ed15},
  intrahash = {70d6cf3c171445620c5024658516ac44},
  issn = {1569-190X},
  journal = {Simulation Modelling Practice and Theory},
  number = 9,
  pages = {1488 - 1502},
  title = {Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis},
  url = {http://www.sciencedirect.com/science/article/pii/S1569190X08001585},
  volume = 16,
  year = 2008
}

@article{Fuchs2010379,
  abstract = {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.},
  author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
  doi = {10.1016/j.neucom.2010.03.022},
  interhash = {88c499ac1dc9e9708e70187967494219},
  intrahash = {fdf6865c1bece3f77cc3e29365a2c6b3},
  issn = {0925-2312},
  journal = {Neurocomputing},
  note = {Artificial Brains},
  number = {1–3},
  pages = {379 - 393},
  title = {Temporal data mining using shape space representations of time series},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
  volume = 74,
  year = 2010
}

@book{ACHH:12,
  address = {Heidelberg, Germany},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {ebf8e8b66c6c0723092e11e40998d61f},
  intrahash = {a0e5d144b39199fa4acb6319f29e7a15},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {Modeling and Mining Ubiquitous Social Media},
  url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6},
  volume = 7472,
  year = 2012
}

@inproceedings{mueller-2012,
  abstract = {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.},
  address = {Washington, DC, USA},
  author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd},
  booktitle = {IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012},
  interhash = {a2695fd9fe6e76b252edbd42d72b34ad},
  intrahash = {90847b1d969ac1ed1f4c8d7146416619},
  publisher = {IEEE},
  title = {Ubicon: Observing Social and Physical Activities},
  year = 2012
}