@inproceedings{williams2019what,
title = {What can we learn from the air chemistry of crowds?},
author = {Jonathan Williams and Christof Stönner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Klüpfel and Nicolas Krauter and Jörg Wicker and Stefan Kramer},
editor = {Armin Hansel and Jürgen Dunkl},
url = {https://www.ionicon.com/sites/default/files/uploads/doc/Contributions_8th-PTR-MS-Conference-2019_web.pdf#page=122},
year = {2019},
date = {2019-05-10},
booktitle = {8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications},
pages = {121-123},
publisher = {Innsbruck University Press},
address = {Innsbruck},
abstract = {Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films.},
keywords = {atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{raza2016trading,
title = {Trading Off Accuracy for Efficiency by Randomized Greedy Warping},
author = {Atif Raza and Jörg Wicker and Stefan Kramer},
url = {https://wicker.nz/nwp-acm/authorize.php?id=N10030
http://doi.acm.org/10.1145/2851613.2851651},
doi = {10.1145/2851613.2851651},
isbn = {978-1-4503-3739-7},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing},
pages = {883-890},
publisher = {ACM},
address = {New York, NY, USA},
series = {SAC '16},
abstract = {Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for f i nding similarity between time series instances.We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification.},
keywords = {data mining, dynamic time warping, time series},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{wicker2015cinema,
title = {Cinema Data Mining: The Smell of Fear},
author = {Jörg Wicker and Nicolas Krauter and Bettina Derstorff and Christof Stönner and Efstratios Bourtsoukidis and Thomas Klüpfel and Jonathan Williams and Stefan Kramer},
url = {https://wicker.nz/nwp-acm/authorize.php?id=N10031
http://doi.acm.org/10.1145/2783258.2783404},
doi = {10.1145/2783258.2783404},
isbn = {978-1-4503-3664-2},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1235-1304},
publisher = {ACM},
address = {New York, NY, USA},
organization = {ACM},
series = {KDD '15},
abstract = {While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.},
keywords = {atmospheric chemistry, breath analysis, causality, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {inproceedings}
}