Publications

[2018] [2017] [2016] [2015] [2014] [2013] [2012] [2011]

2018

Kibanov, M., Becker, M., Mueller, J., Atzmueller, M., Hotho, A., Stumme, G.: Adaptive kNN Using Expected Accuracy for Classification of Geo-spatial Data. Proceedings of the 33rd Annual ACM Symposium on Applied Computing. p. 857--865. ACM, Pau, France (2018).
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The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e. g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary similarity function can be used to find these observations. We introduce and evaluate different similarity functions. For the evaluation, we use five different classification tasks based on geo-spatial data. Each classification task consists of (tens of) thousands of items. We demonstrate, that the presented expected accuracy measures can be a good estimator for kNN performance, and the proposed adaptive kNN classifier outperforms common kNN and previously introduced adaptive kNN algorithms. Also, we show that the range of considered k can be significantly reduced to speed up the algorithm without negative influence on classification accuracy.
Werdin, F., Tenenhaus, M., Becker, M., Rennekampff, H.-O.: Healing Time Correlates With the Quality of Scaring: Results From a Prospective Randomized Control Donor Site Trial. Dermatologic Surgery. 44, 521--527 (2018).
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2017

Atzmueller, M., Becker, M., Mueller, J.: Collective Sensing Platforms. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D.P., Stumme, G., Tria, F., and Theunis, J. (eds.) Participatory Sensing, Opinions and Collective Awareness. p. 115--133. Springer International Publishing, Cham (2017).
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Zehe, A., Becker, M., Jannidis, F., Hotho, A.: Towards Sentiment Analysis on German Literature. Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz). p. 387--394. Springer (2017).
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Lemmerich, F., Singer, P., Becker, M., Espin-Noboa, L., Dimitrov, D., Helic, D., Hotho, A., Strohmaier, M.: Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. p. 354--357. Springer (2017).
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Niebler, T., Becker, M., Pölitz, C., Hotho, A.: Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. In: Nikitina, N., Song, D., Fokoue, A., and Haase, P. (eds.) Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017) (2017).
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Sperlich, B., Becker, M., Hotho, A., Wallmann-Sperlich, B., Sareban, M., Winkert, K., Steinacker, J.M., Treff, G.: Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study. Frontiers in Physiology. 8, 655 (2017).
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The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e. sedentary: <1.5 MET; light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e. > 480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-wk preparation period was assessed by a wrist-worn multisensory device (Microsoft Band II (MSBII)). A total of 11 rowers provided valid data (i.e. > 480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63±1.25 hours per day during the week and 12.49±1.10 hours per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27±1.15, 0.76±0.37, 0.51±0.44 hours per day and 0.67±0.43, 0.59±0.37, 0.53±0.32 hours per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69) Based on our pilot study of eleven national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 hours/day.
Treff, G., Winkert, K., Sareban, M., Steinacker, J.M., Becker, M., Sperlich, B.: Eleven-Week Preparation Involving Polarized Intensity Distribution Is Not Superior to Pyramidal Distribution in National Elite Rowers. Frontiers in Physiology. 8, 515 (2017).
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Polarized (POL) training intensity distribution (TID) emphasizes high-volume low-intensity exercise in zone (Z)1 (< first lactate threshold) with a greater proportion of high-intensity Z3 (> second lactate threshold) compared to Z2 (between first and second lactate threshold). In highly trained rowers there is a lack of prospective controlled evidence whether POL is superior to pyramidal (PYR; i.e. greater volume in Z1 vs. Z2 vs. Z3) TID. The aim of the study was to compare the effect of POL vs. PYR TID in rowers during an 11-wk preparation period. Fourteen national elite male rowers participated (age: 20 ± 2 years, maximal oxygen uptake (⩒O2max): 66±5 mL/min/kg). The sample was split into PYR and POL by varying the percentage spent in Z2 and Z3 while Z1 was clamped to ~93\% and matched for total and rowing volume. Actual TIDs were based on time within heart rate zones (Z1 and Z2) and duration of Z3-intervals. The main outcome variables were average power in 2000 m ergometer-test (P2000m), power associated with 4 mmol/L blood lactate (P4BLa), and ⩒O2max. To quantify the level of polarization, we calculated a Polarization-Index as log (\%Z1 x \%Z3/\%Z2). PYR and POL did not significantly differ regarding rowing or total volume, but POL had a higher percentage of Z3 intensities (6±3\% vs. 2±1\%; p < .005) while Z2 was lower (1±1\% vs. 3±2\%; p < .05) and Z1 was similar (94±3\% vs. 93±2\%, p = .37). Consequently, Polarization-Index was significantly higher in POL (3.0±0.7 a.u. vs. 1.9±0.4 a.u.; p < .01) P2000m did not significantly change with PYR (1.5±1.7\%, p = .06) nor POL (1.5±2.6\%, p = .26). ⩒O2max did not change (1.7±5.6\%, p = .52 or 0.6±2.6, p = .67) and a small increase in P4BLa was observed in PYR only (1.9±4.8\%, p = .37 or -0.5±4.1\%, p = .77). Changes from pre to post were not significantly different between groups in any performance measure. POL did not prove to be superior to PYR, possibly due to the high and very similar percentage of Z1 in this study.
Becker, M., Lemmerich, F., Singer, P., Strohmaier, M., Hotho, A.: MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. Data Mining and Knowledge Discovery. 31, 1359--1390 (2017).
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Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.
Atzmueller, M., Becker, M., Molino, A., Mueller, J., Peters, J., Sirbu, A.: Applications for Environmental Sensing in EveryAware. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D.P., Stumme, G., Tria, F., and Theunis, J. (eds.) Participatory Sensing, Opinions and Collective Awareness. p. 135--155. Springer International Publishing, Cham (2017).
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Gravino, P., Sirbu, A., Becker, M., Servedio, V.D.P., Loreto, V.: Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson. Participatory Sensing, Opinions and Collective Awareness. p. 337--362. Springer International Publishing, Cham (2017).
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2016

Niebler, T., Schlör, D., Becker, M., Hotho, A.: Extracting Semantics from unconstrained navigation on wikipedia. KI - Künstliche Intelligenz. 30, 163--168 (2016).
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Niebler, T., Becker, M., Zoller, D., Doerfel, S., Hotho, A.: FolkTrails: Interpreting navigation behavior in a social tagging system. International on Conference on Information and Knowledge Management. p. 2311--2316. ACM, New York, NY, USA (2016).
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Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users' needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.
Zehe, A., Becker, M., Hettinger, L., Hotho, A., Reger, I., Jannidis, F.: Prediction of Happy Endings in German Novels. In: Cellier, P., Charnois, T., Hotho, A., Matwin, S., Moens, M.-F., and Toussaint, Y. (eds.) Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016. pp. 9-16 (2016).
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Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73\%.
Lemmerich, F., Becker, M., Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Mining Subgroups with Exceptional Transition Behavior. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p. 965--974. ACM, San Francisco, California, USA (2016).
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We present a new method for detecting interpretable subgroups with exceptional transition behavior in sequential data. Identifying such patterns has many potential applications, e.g., for studying human mobility or analyzing the behavior of internet users. To tackle this task, we employ exceptional model mining, which is a general approach for identifying interpretable data subsets that exhibit unusual interactions between a set of target attributes with respect to a certain model class. Although exceptional model mining provides a well-suited framework for our problem, previously investigated model classes cannot capture transition behavior. To that end, we introduce first-order Markov chains as a novel model class for exceptional model mining and present a new interestingness measure that quantifies the exceptionality of transition subgroups. The measure compares the distance between the Markov transition matrix of a subgroup and the respective matrix of the entire data with the distance of random dataset samples. In addition, our method can be adapted to find subgroups that match or contradict given transition hypotheses. We demonstrate that our method is consistently able to recover subgroups with exceptional transition models from synthetic data and illustrate its potential in two application examples. Our work is relevant for researchers and practitioners interested in detecting exceptional transition behavior in sequential data.
Becker, M., Mewes, H., Hotho, A., Dimitrov, D., Lemmerich, F., Strohmaier, M.: SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails. International Conference Companion on World Wide Web. p. 17--18. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2016).
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2015

Becker, M., Singer, P., Lemmerich, F., Hotho, A., Helic, D., Strohmaier, M.: Photowalking the City: Comparing Hypotheses About Urban Photo Trails on Flickr. In: Liu, T.-Y., Scollon, C.N., and Zhu, W. (eds.) International Conference on Social Informatics. p. 227--244. Springer International Publishing, Cham (2015).
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Becker, M., Singer, P., Lemmerich, F., Hotho, A., Helic, D., Strohmaier, M.: VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories. Conference on Hypertext & Social Media. p. 319--320. ACM, Guzelyurt, Northern Cyprus (2015).
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Ring, M., Otto, F., Becker, M., Niebler, T., Landes, D., Hotho, A.: ConDist: A Context-Driven Categorical Distance Measure. In: Appice, A., Rodrigues, P.P., Costa, V.S., Soares, C., Gama, J., and Jorge, A. (eds.) Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). p. 251--266. Springer International Publishing, Cham (2015).
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A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging task since the values within such attributes have no inherent order, especially without additional domain knowledge. In this paper, we propose an unsupervised distance measure for objects with categorical attributes based on the idea that categorical attribute values are similar if they appear with similar value distributions on correlated context attributes. Thus, the distance measure is automatically derived from the given data set. We compare our new distance measure to existing categorical distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.
Sirbu, A., Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F., Van den Bossche, J.: Participatory patterns in an international air quality monitoring initiative. PLoS ONE. 10, e0136763 (2015).
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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.
Atzmueller, M., Mueller, J., Becker, M.: Exploratory Subgroup Analytics on Ubiquitous Data. In: Atzmueller, M., Chin, A., Scholz, C., and Trattner, C. (eds.) Mining, Modeling and Recommending 'Things' in Social Media. p. 1--20. Springer International Publishing, Cham (2015).
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Hettinger, L., Becker, M., Reger, I., Jannidis, F., Hotho, A.: Genre Classification on German Novels. 26th International Workshop on Database and Expert Systems Applications (DEXA). p. 249--253 (2015).
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Becker, M., Borchert, K., Hirth, M., Mewes, H., Hotho, A., Tran-Gia, P.: MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform. International Conference on Knowledge Technologies and Data-driven Business. p. 10:1--10:8. ACM, New York, NY, USA (2015).
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Atzmueller, M., Mueller, J., Becker, M.: Exploratory Subgroup Analytics on Ubiquitous Data. In: Atzmueller, M., Chin, A., Scholz, C., and Trattner, C. (eds.) Mining, Modeling, and Recommending 'Things' in Social Media. p. 1--20. Springer International Publishing, Cham (2015).
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This paper presents exploratory subgroup analytics on ubiquitous data: We propose subgroup discovery and assessment approaches for obtaining interesting descriptive patterns and provide a novel graph-based analysis approach for assessing the relations between the obtained subgroup set. This exploratory visualization approaches allows for the comparison of subgroups according to their relations to other subgroups and to include further parameters, e.g., geo-spatial distribution indicators. We present and discuss analysis results utilizing real-world data given by geo-tagged noise measurements with associated subjective perceptions and a set of tags describing the semantic context.

2014

Becker, M., Hotho, A., Mueller, J., Kibanov, M., Atzmueller, M., Stumme, G.: Subjective vs. Objective Data: Bridging the Gap, http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/, (2014).
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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.
Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its applications for ubiquitous social computing. New Review of Hypermedia and Multimedia. 20, 53--77 (2014).
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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.

2013

Becker, M., Mueller, J., Hotho, A., Stumme, G.: A Generic Platform for Ubiquitous and Subjective Data. Conference on Pervasive and Ubiquitous Computing Adjunct Publication. p. 1175--1182. , New York, NY, USA (2013).
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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.
Becker, M., Caminiti, S., Fiorella, D., Francis, L., Gravino, P., Haklay, M. (M.), Hotho, A., Loreto, V., Mueller, J., Ricchiuti, F., Servedio, V.D.P., Sirbu, A., Tria, F.: Awareness and learning in participatory noise sensing. PLOS ONE. 8, e81638 (2013).
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The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.
Mueller, J., Doerfel, S., Becker, M., Hotho, A., Stumme, G.: Tag Recommendations for SensorFolkSonomies. Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013). CEUR-WS (2013).
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With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
Lemmerich, F., Becker, M., Puppe, F.: Difference-Based Estimates for Generalization-Aware Subgroup Discovery. In: Blockeel, H., Kersting, K., Nijssen, S., and Zelezny, F. (eds.) Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). p. 288--303. Springer, Berlin, Heidelberg (2013).
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2012

Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: Observing Social and Physical Activities. International Conference on Green Computing and Communications. p. 317--324. IEEE, Washington, DC, USA (2012).
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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.
Lemmerich, F., Becker, M., Atzmueller, M.: Generic Pattern Trees for Exhaustive Exceptional Model Mining. In: Flach, P.A., De Bie, T., and Cristianini, N. (eds.) European Conference on Machine Learning and Knowledge Discovery in Databases. p. 277--292. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.

2011

Becker, M.: Constraint Based Descriptive Pattern Mining, http://www.is.informatik.uni-wuerzburg.de/fileadmin/10030600/staff/becker/20110713_becker_constraint-based-descriptive-pattern-mining_diplomathesis.pdf, (2011).
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