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A1  Data Mining for Ubiquitous System Software


chen.jpg
Prof. Dr. Chen, Jian Jia
Morik.JPG
Prof. Dr. Morik, Katharina

Project A1 develops data analysis methods for embedded systems. Learning methods are to consume resources minimally so that they can be executed by embedded systems. A1 contributes to these overall goals by developing the theoretical basis of machine learning under resource constraints. In particular, it investigates machine learning models that formalise implicit functional relations within the data. More precisely, a model, e.g., from the class of linear models, exponential families, decision trees, or convolutional networks, specifies the general formal basis of any machine learning method. It determines implicitly what is learned (e.g., classifier, probability density, clustering model), and from what it is learned (e.g., real-valued vectors, categorical data, count data). Instances of a particular model are assessed by a quality measure (e.g., root mean square error, F β -score, correct classification rate, or the likelihood). In A1, we extend this notion of quality by adding simplicity of model representation and simplicity of inference, where simplicity is relative to the underlying hardware architecture. Thus, the quality of a model may comprise its memory consumption, numerical precision, parameter integrality, or approximability. Since learning itself is often expressed by one or more optimisation problems, resource constraints are added to the quality measure via regularisation. Several algorithms exist that can find (approximate) solutions to optimisation problems. Finally, the algorithms are executed on a specific hardware platform. Each of these layers, i.e., platforms, algorithms, models, is studied in the research centre. Project A3 starts with the execution on a certain platform, project A2 moves on to algorithms, and project A1 investigates the models of learning.

There is a plentitude of heuristics that algorithmically save resources. However, heuristics most often come without guarantees. In the A1 project, we specify restrictions at the model level and analyse the impact of the restriction on the quality. Moreover, a model of learning with proven guarantees makes explicit the link from the restrictions of the model to those of constrained hardware. It is a long way to achieve such models with guarantees. For the class of learning within the exponential family, we succeeded in developing spatiotemporal random fields, integer Markov random fields, and a novel quadrature based on first principles, each focusing on specific constraints. Proven properties of the novel models conclude the basic research on exponential families. At the same time, applications in other projects have already shown their practical usefulness. For top-down decision trees, the journey has just begun, and first publications show the potential of linking tree models of learning to FPGAs.

Project management:

Prof. Dr. Chen, Jian Jia
Prof. Dr. Morik, Katharina

Project members:

Buschjäger, Sebastian
Lochmann, Alexander
Dr. Piatkowski, Nico
Dr. Schirmeier, Horst

Alumni project management:


Spinczyk.JPG
Prof. Dr. Spinczyk, Olaf

Alumni:

Borghorst, Hendrik
Dr. Duivesteijn, Wouter
Dr. Jungermann, Felix
Dr. Liebig, Thomas
Nassour, Orwa
Dr. Pölitz, Christian
Streicher, Jochen

Datasets:

Android Load Profile Data
MobiDACSummerschool2012

Software:

Architectural specific Random Forests
CiAO/IP: A Highly Configurable Aspect-Oriented IP Stack
Corpus Linguistics Plugin LDA
Corpus Linguistics Plugin from KobRA
Corpus Linguistics Plugin from KobRA
Linear-Chain Conditional Random Fields (CRFs) for GPUs
RapidMiner Information Extraction Plugin
RapidMiner TaCl Extension
Spatio-Temporal Random Fields (STRF)
Stochastic Gradient Descent on Stiefel Manifolds
Streams Framework
SytemTap4Android
k-means Clustering von Zeitreihen mit Dynamic Time Warping
kCQL - kernel Continous Query Language

Publications:

Bunse/Piatkowski/2018a Bunse, Mirko and Piatkowski, Nico and Morik, Katharina. Towards a Unifying View on Deconvolution in Cherenkov Astronomy. In Gemulla, Rainer and Ponzetto, Simone Paolo and Bizer, Christian and Keuper, Margret and Stuckenschmidt, Heiner (editors), Lernen, Wissen, Daten, Analysen (LWDA) conference proceedings, Vol. 2191, pages 73--77, Gemulla, Rainer and Ponzetto, Simone Paolo and Bizer, Christian and Keuper, Margret and Stuckenschmidt, Heiner, 2018.


Bunse/Piatkowski/2018b Bunse, Mirko and Piatkowski, Nico and Ruhe, Tim and Rhode, Wolfgang and Morik, Katharina. Unification of Deconvolution Algorithms for Cherenkov Astronomy. In 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2018.


Buschjaeger/2018a Buschjäger, Sebastian and Chen, Kuan-Hsun and Chen, Jian-Jia, Morik, Katharina. Realization of Random Forest for Real-Time Evaluation through Tree Framing. In The IEEE International Conference on Data Mining series (ICDM), 2018.


Buschjaeger/Morik/2017b Buschjäger, Sebastian and Morik, Katharina. Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. In IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 65-I, No. 1, pages 209--222, 2018.


Falkenberg/etal/2018b Robert Falkenberg and Benjamin Sliwa and Nico Piatkowski and Christian Wietfeld. Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators. In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, 2018.


Hess/etal/2018a Hess, Sibylle and Piatkowski, Nico and Morik, Katharina. The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. In Proceedings of the 2018 SIAM International Conference on Data Mining, SDM 2018, May 3-5, 2018, San Diego Marriott Mission Valley, San Diego, CA, USA., pages 405--413, SIAM, 2018.


Hoelscher/etal/2018a Hölscher, Nils and Chen, Kuan-Hsun and von der Brüggen, Georg and Chen, Jian-Jia. Examining and Supporting Multi-Tasking in EV3OSEK. In 14th annual workshop on Operating Systems Platforms for Embedded Real-Time applications, OSPERT 2018, July 3, 2018. Barcelona, Spain, 2018.


Piatkowski/Morik/2018a Piatkowski, Nico and Morik, Katharina. Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, California, USA, August 6-10, 2018, 2018.


Sliwa/etal/2018e Benjamin Sliwa and Nico Piatkowski and Marcus Haferkamp and Dennis Dorn and Christian Wietfeld. Leveraging the channel as a sensor: Real-time vehicle classification using multidimensional radio-fingerprinting. In 2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 2018.


VonDerBrueggen/etal/2018a von der Brüggen, Georg and Piatkowski, Nico and Chen, Kuan-Hsun and Chen, Jian-Jia and Morik, Katharina. Efficiently Approximating the Probability of Deadline Misses in Real-Time Systems. In 30th Euromicro Conference on Real-Time Systems, ECRTS 2018, July 3-6, 2018, Barcelona, Spain, LIPIcs, 2018.


Buschjaeger/Morik/2017a Buschjäger, Sebastian and Morik, Katharina and Schmidt, Maik. Summary Extraction on Data Streams in Embedded Systems. In Proceedings of the ECML Workshop on IoT Large Scale Learning From Data Streams, ceur-ws.org, 2017.


Hess/etal/2017a Hess, Sibylle and Morik, Katharina and Piatkowski, Nico. The PRIMPING routine---Tiling through proximal alternating linearized minimization. In Data Mining and Knowledge Discovery, Vol. 31, No. 4, pages 1090--1131, 2017.


Liebig/etal/2017b Liebig, Thomas and Piatkowski, Nico and Bockermann, Christian and Morik, Katharina. Dynamic Route Planning with Real-Time Traffic Predictions. In Information Systems, Vol. 64, pages 258--265, Elsevier, 2017.


Lochmann/etal/2017a Lochmann, Alexander and Bruckner, Fabian and Spinczyk, Olaf. Reproducible Load Tests for Android Systems with Trace-based Benchmarks. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pages 73--76, New York, NY, USA, ACM Press, 2017.


Morik/etal/2017a Morik, Katharina and Bockermann, Christian and Buschjäger, Sebastian. Big Data Science. In German journal on Artificial Intelligence (KI 2017), Vol. 32, No. 1, pages 27--36, 2017.


Buschjaeger/2016b Sebastian Buschjäger. Online Gauß-Prozesse zur Regression auf FPGAs. No. 1, TU Dortmund, 2016.


Erdmann/etal/2016b Erdmann, Elena and Boczek, Karin and Koppers, Lars and von Nordheim, Gerret and Poelitz, Christian and Molina, Alejandro and Morik, Katharina and Mueller, Henrik and Rahnenfuehrer, Joerg and Kersting, Kristian. Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage. In Proceedings of the 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, 2016.


Piatkowski/etal/2016a Piatkowski, Nico and Lee, Sangkyun and Morik, Katharina. Integer undirected graphical models for resource-constrained systems. In Neurocomputing, Vol. 173, No. 1, pages 9--23, Elsevier, 2016.


Piatkowski/Morik/2016a Piatkowski, Nico and Morik, Katharina. Stochastic Discrete Clenshaw-Curtis Quadrature. In Proceedings of the 33rd International Conference on Machine Learning, ICML 2016, New York, USA, 19-24 June 2016, JMLR.org, 2016.


Piatkowski/Schnitzler/2016a Piatkowski, Nico and Schnitzler, Fran\ccois. Compressible Reparametrization of Time-Variant Linear Dynamical Systems. In Solving Large Scale Learning Tasks. Challenges and Algorithms - Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday, pages 234--250, 2016.


Poelitz/etal/2016a Poelitz, Christian and Duivesteijn, Wouter and Morik, Katharina. Interpretable Domain Adaptation via Optimization over the Stiefel Manifold. In Machine Learning, Vol. 104, No. 2-3, pages 315-336, 2016.


Stolpe/etal/2016a Stolpe, Marco and Blom, Hendrik and Morik, Katharina. Sustainable Industrial Processes by Embedded Real-Time Quality Prediction. In Computational Sustainability, pages 201--243, Springer, 2016.


Downar/Duivesteijn/2015a Downar, Lennart and Duivesteijn, Wouter. Exceptionally Monotone Models - the Rank Correlation Model Class for Exceptional Model Mining. In Data Mining (ICDM), 2015 IEEE International Conference on, pages 111-120, IEEE, IEEE Computer Society, 2015.


Morik/etal/2015a Morik, Katharina and Jung, Alexander and Weckwerth, Jan and Rötner, Stefan and Hess, Sibylle and Buschjäger, Sebastian and Pfahler, Lukas. Untersuchungen zur Analyse von deutschsprachigen Textdaten. No. 2, Technische Universität Dortmund, 2015.


Streicher/etal/2015a Streicher, Jochen and Lochmann, Alexander and Spinczyk, Olaf. kCQL: Declarative Stream-based Acquisition and Processing of Diagnostic OS Data. In Proceedings of the Conference on Timely Results in Operating Systems (TRIOS), 2015.


Artikis/etal/2014a Alexander Artikis and Matthias Weidlich and Francois Schnitzler and Ioannis Boutsis and Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik and Vana Kalogeraki and Jakub Marecek and Avigdor Gal and Shie Mannor and Dimitrios Gunopulos and Dermot Kinane. Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management. In Proceedings of the 17th International Conference on Extending Database Technology, 2014.


Bockermann/2014a Christian Bockermann. A Visual Programming Approach to Big Data Analytics. In Proc. HCI International 2014, 2014.


Bockermann/2014b Christian Bockermann. A Survey of the Stream Processing Landscape. No. 6, TU Dortmund University, 2014.


Lee/Poelitz/2014a Lee, Sangkyun and Pölitz, Christian. Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-Limited Sensor Networks. In International Conference on Pattern Recognition Applications and Methods, 2014.


Liebig/etal/2014d Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik. Route Planning with Real-Time Traffic Predictions. In Proceedings of the LWA 2014 Workshops: KDML, IR, FGWM, pages 83-94, 2014.


Piatkowski/etal/2014a Piatkowski, Nico and Sangkyun, Lee and Morik,Katharina. The Integer Approximation of Undirected Graphical Models. In De Marsico, Maria and Tabbone, Antoine and Fred, Ana (editors), ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, ESEO, Angers, Loire Valley, France, 6-8 March, 2014, pages 296--304, SciTePress, 2014.


Piatkowski/etal/2015b Piatkowski, Nico and Streicher, Jochen and Olaf, Spinczyk and Morik, Katharina. Open Smartphone Data for Structured Mobility and Utilization Analysis in Ubiquitous Systems. In Atzmueller, Martin and Chin, Alvin and Scholz, Christoph and Trattner, Christoph (editors), Mining, Modeling and Recommending Things in Social Media, Vol. 8940, pages 116 -- 130, Springer, 2014.


Piatkowski/Morik/2014a Piatkowski, Nico and Morik, Katharina. Towards an Integer Approximation of Undirected Graphical Models. In Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014., pages 123--124, 2014.


Schnitzler/etal/2014b Schnitzler, Francois and Artikis, Alexander and Weidlich, Matthias and Boutsis, Ioannis and Liebig, Thomas and Piatkowski, Nico and Bockermann, Christian and Morik, Katharina and Kalogeraki, Vana and Marecek, Jakub and Gal, Avigdor and Mannor, Shie and Kinane, Dermot and Gunopulos, Dimitrios. Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights. In Proceedings of the European Conference on Machine Learning (ECML), Nectar Track, pages 520-523, Springer, 2014.


Streicher/2014a Jochen Streicher. Data Modeling of Ubiquitous System Software. No. 7, TU Dortmund University, 2014.


Buschhoff/etal/2013a Buschhoff, Markus and Streicher, Jochen and Dusza, Björn and Wietfeld, Christian and Spinczyk, Olaf. MobiSIM: A Simulation Library for Resource Prediction of Smartphones and Wireless Sensor Networks. In Proceedings of the 46th Annual Simulation Symposium, Society for Computer Simulation International, 2013.


Piatkowski/etal/2013a Piatkowski, N. and Lee, S. and Morik, K.. Spatio-temporal random fields: compressible representation and distributed estimation. In Machine Learning, Vol. 93, No. 1, pages 115--139, Springer, 2013.


Ruhe/etal/2013a Ruhe, Tim and Morik, Katharina and Rhode, Wolfgang. Application of RapidMiner in Neutrino Astronomy. In Hofmann, Markus and Klinkenberg, Ralf (editors), RapidMiner: Data Mining Use Cases and Business Analytics Applications, CRCPress Book, 2013.


streicher/etal/2013a Streicher, Jochen and Nassour, Orwa and Spinczyk, Olaf. System Support for Privacy-preserving and Energy-efficient Data Gathering in the Global Smartphone Network -- Opportunities and Challenges. In Proceedings of the 3rd International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS '13), pages 80--85, SciTePress, 2013.


Streicher/etal/2013b Streicher, Jochen and Piatkowski, Nico and Morik, Katharina and Spinczyk, Olaf. Open Smartphone Data for Mobility and Utilization Analysis in Ubiquitous Environments. In Atzmüller, Martin and Scholz, Christoph (editors), Proceedings of the 4th International Workshop on Mining Ubiquitous and Social Environments (MUSE), 2013.


Bockermann/Blom/2012a Christian Bockermann and Hendrik Blom. Processing Data Streams with the RapidMiner Streams-Plugin. In RapidMiner Community Meeting and Conference, 2012.


Bockermann/Blom/2012c Christian Bockermann and Hendrik Blom. The Streams Framework. No. 5, TU Dortmund, 2012.


Borchert/etal/2012a Borchert, Christoph and Lohmann, Daniel and Spinczyk, Olaf. CiAO/IP: A Highly Configurable Aspect-Oriented IP Stack. In Proceedings of the 10th international conference on Mobile systems, applications, and services, pages 435--448, New York, NY, USA, ACM, 2012.


Lohmann/etal/2012a Daniel Lohmann and Olaf Spinczyk and Wanja Hofer and Wolfgang Schröder-Preikschat. The Aspect-Aware Design and Implementation of the CiAO Operating-System Family. In Gary T. Leavens and Shigeru Chiba and Michael Haupt and Klaus Ostermann and Eric Wohlstadter (editors), Transactions on AOSD IX, No. 7271, pages 168--215, Springer, 2012.


Michaelis/etal/2012a Michaelis, Stefan and Piatkowski, Nico and Morik, Katharina. Predicting Next Network Cell IDs for Moving Users with Discriminative and Generative Models. In Mobile Data Challenge by Nokia Workshop in conjunction with Int. Conf. on Pervasive Computing, Newcastle, UK, 2012.


Morik/etal/2011a Morik, Katharina and Kaspari, Andreas and Wurst, Michael and Skirzynski, Marcin. Multi-Objective Frequent Termset Clustering. In Knowledge and Information Systems, Vol. 30, No. 3, pages 715-738, 2012.


Piatkowski/2012a Piatkowski, Nico. iST-MRF: Interactive Spatio-Temporal Probabilistic Models for Sensor Networks. In International Workshop at ECML PKDD 2012 on Instant Interactive Data Mining (IID), 2012.


Piatkowski/etal/2012a Piatkowski, Nico and Lee, Sangkyun and Morik, Katharina. Spatio-Temporal Models For Sustainability. In Marwah, Manish and Ramakrishnan, Naren and Berges, Mario and Kolter, Zico (editors), Proceedings of the SustKDD Workshop within ACM KDD 2012, ACM, 2012.


Fricke/etal/2011a Fricke, Peter and Jungermann, Felix and Morik, Katharina and Piatkowski, Nico and Spinczyk, Olaf and Stolpe, Marco and Streicher, Jochen. Towards Adjusting Mobile Devices To User's Behaviour. In Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin (editors), Analysis of Social Media and Ubiquitous Data, Vol. 6904, pages 99--118, Berlin, Heidelberg, Springer, 2011.


Jungermann/2011a Jungermann, Felix. Handling Tree-Structured Values in RapidMiner. In Proceedings of the 2nd RapidMiner Community Meeting and Conference (RCOMM 2011), pages 151 -- 162, 2011.


Jungermann/2011b Jungermann, Felix. Tree Kernel Usage in Naive Bayes Classifiers. In Proceedings of the LWA 2011, 2011.


Jungermann/2011c Jungermann, Felix. Documentation of the Information Extraction Plugin for RapidMiner. 2011.


Michaelis/2011a Stefan Michaelis. Balancing High-Load Scenarios with Next Cell Predictions and Mobility Pattern Recognition. In International Conference on Mobile Services, Resources, and Users (MOBILITY 2011), Barcelona, Spain, 2011.


Piatkowski/2011b Piatkowski, Nico. Probabilistic Graphical Models in RapidMiner. No. 2, TU Dortmund University, 2011.


Piatkowski/2011c Piatkowski, Nico. Parallel Algorithms for GPU Accelerated Probabilistic Inference. In International Workshop on Big Learning, Neural Information Processing Systems (NIPS), 2011.


Piatkowski/Morik/2011a Piatkowski, Nico and Morik, Katharina. Parallel Inference on Structured Data with CRFs on GPUs. In International Workshop at ECML PKDD on Collective Learning and Inference on Structured Data (COLISD), Athens, Greece, 2011.


Piatkowski/Morik/2011b Piatkowski, Nico and Morik, Katharina. Parallel Loopy Belief Propagation in Conditional Random Fields. In KDML Workshop of the LWA2011, Magdeburg, Germany, 2011.


Ruhe/etal/2011a Ruhe, Tim and Morik, Katharina and Schowe,Benjamin. Data Mining on Ice. In Sarro, Luis and Bailer-Jones, Coryn and Eyer, Laurent and O'Mullane, William and de Ridder, Joris (editors), Procs. of the Workshop on Astrostatistics and Data Mining in Large Astronomical Databases, 2011.


Ruhe/etal/2011b Ruhe, Tim and Morik, Katharina and Rhode, Wolfgang. Data Mining Ice Cubes. In Gabriel, Carlos et alii (editors), Astronomical Data Analysis Software and Systems, 2011.


Streicher/Spinczyk/2011a Streicher, Jochen and Spinczyk, Olaf. Energy-Efficient GPS-Based Positioning in the Android Operating System. No. 3, TU Dortmund University, 2011.



Disserations:

  • Piatkowski/2018a - Exponential Families on Resource-Constrained Systems
  • Poelitz/2016d - Automatic methods to extract latent meanings in large text corpora
  • Bockermann/2015a - Mining Big Data Streams for Multiple Concepts
  • Jungermann/2012a - About the Exploration of Data Mining Techniques using Structured Features for Information Extraction

Final Theses:

Hildebrandt/2018a Hildebrandt, Hendrik. Deep Reinforcement Learning zur Minderung von Verspätungen im ÖPNV. TU Dortmund, 2018.


Pang/2018a Pang, Weihan. A Machine Learning Approach for Aspect-based Sentiment Analysis on Social Media. TU Dortmund, 2018.


Kilian/2017a Kilian, Phillip. Visualisierung von Embeddings zur Analyse großer Dokumenten-Kollektionen. 2017.


Peter/2017a Peter, Sebastian. Multi-modal Route Planning with Dynamic Public Transport Delays. TU Dortmund, 2017.


Sitta/2017a Sitta, Andreas. Entdecken von Themen und Communitys in Online-Foren. 2017.


Buschjaeger/2016a Buschjäger, Sebastian. Online Gauß-Prozesse zur Regression auf FPGAs. TU Dortmund, 2016.


Heppe/2016a Heppe, Lukas. ÖPNV Verspätungsvorhersage mit probabilistischen graphischen Modellen. 2016.


Sotzny/2016a Sotzny, Maurice. Dynamic Routing with Bandit Feedback Learning. TU Dortmund, 2016.


Vishnyakova/2016a Vishnyakova, Liudmila. Untersuchung von Poisson- und Multinomialverteilungsbasierten Modellen für die Analyse von Verkehrsdaten. 2016.


Ahmels/2015a Ahmels, Eike. Kernmengenbasiertes Clustering von Datenströmen auf Android- basierten Smartphones. TU Dortmund, 2015.


Langenberg/2015a Langenberg, Jonas. Erkennung ungewöhnlicher Textbelege in großen Textkorpora mittels minimum enclosing Balls. 2015.


Pfahler/2015a Pfahler, Lukas. Explicit and Implicit Feature Maps for Structured Output Prediction. TU Dortmund, 2015.


Risse/2015a Sarah Risse. Untersuchung latenter Strukturen im Parameterraum maschineller Lernverfahren. TU Dortmund, Computer Science 8, 2015.


Schendekehl/2015a Schendekehl, Tim. Implementierung von Konfidenz-basierten Aktivenlern-Methoden in RapidMiner. TU Dortmund, 2015.


Asmi/2014a Mohamed Asmi. Nutzungsmuster in Datenströmen Android-basierter Smartphones. TU Dortmund, 2014.


Downar/2014a Lennart Downar. A Rank Correlation Model Class for Exceptional Model Mining. TU Dortmund, 2014.


Kloeckner/2013a Tim Klöckner. Betriebssystemseitige Energieoptimierung mobiler Datenkommunikation durch Paketbündelung. TU Dortmund, CS12, 2013.


Masarwa/2013a Aiser Masarwa. Untersuchung der Energieeffizienz verschiedener Methoden der Datenerhebung auf Android-Smartphones. TU Dortmund, CS12, 2013.


Matuschek/2013a Matuschek, Christian. Symbolisierung und Clustering von Zeitreihen als neue Operatoren im ValueSeries Plugin von Rapidminer. 2013.


Pfahler/2013a Pfahler,Lukas. Effizienteres k-means Clustering von Zeitreihen mit Dynamic Time Warping durch kaskadiertes Berechnen von unteren Schranken. 2013.


Skirzynski/2013a Skirzynski, Adrian. Aggregation häufiger Mengen in Datenströmen. TU Dortmund, 2013.


Spain/2013a Spain, David. A Survey on Subspace Clustering. TU Dortmund, 2013.


Egorov/2012a Egorov, Alexey. Logistic regression with group ell 1 vs. elastic net regularization. TU Dortmund, 2012.


Kokott/2012a Kokott, Markus. Verkehrsvorhersage unter Verwendung von Methoden des maschinellen Lernens. 2012.


Skirzynski/2012a Skirzynski, Marcin. Subspace-Clustering mit parallelen häufigen Mengen. TU Dortmund, LS 8, 2012.


Piatkowski/2011a Piatkowski, Nico. Parallele Implementierung von Conditional Random Fields unter Verwendung von General-Purpose computation on Graphics Processing Units. Technische Universität Dortmund, 2011.


  • Hildebrandt/2018a - Deep Reinforcement Learning zur Minderung von Verspätungen im ÖPNV
  • Pang/2018a - A Machine Learning Approach for Aspect-based Sentiment Analysis on Social Media
  • Kilian/2017a - Visualisierung von Embeddings zur Analyse großer Dokumenten-Kollektionen
  • Peter/2017a - Multi-modal Route Planning with Dynamic Public Transport Delays
  • Sitta/2017a - Entdecken von Themen und Communitys in Online-Foren
  • Buschjaeger/2016a - Online Gauß-Prozesse zur Regression auf FPGAs
  • Heppe/2016a - ÖPNV Verspätungsvorhersage mit probabilistischen graphischen Modellen
  • Sotzny/2016a - Dynamic Routing with Bandit Feedback Learning
  • Vishnyakova/2016a - Untersuchung von Poisson- und Multinomialverteilungsbasierten Modellen für die Analyse von Verkehrsdaten
  • Ahmels/2015a - Kernmengenbasiertes Clustering von Datenströmen auf Android- basierten Smartphones
  • Langenberg/2015a - Erkennung ungewöhnlicher Textbelege in großen Textkorpora mittels minimum enclosing Balls
  • Pfahler/2015a - Explicit and Implicit Feature Maps for Structured Output Prediction
  • Risse/2015a - Untersuchung latenter Strukturen im Parameterraum maschineller Lernverfahren
  • Schendekehl/2015a - Implementierung von Konfidenz-basierten Aktivenlern-Methoden in RapidMiner
  • Asmi/2014a - Nutzungsmuster in Datenströmen Android-basierter Smartphones
  • Downar/2014a - A Rank Correlation Model Class for Exceptional Model Mining
  • Kloeckner/2013a - Betriebssystemseitige Energieoptimierung mobiler Datenkommunikation durch Paketbündelung
  • Masarwa/2013a - Untersuchung der Energieeffizienz verschiedener Methoden der Datenerhebung auf Android-Smartphones
  • Matuschek/2013a - Symbolisierung und Clustering von Zeitreihen als neue Operatoren im ValueSeries Plugin von Rapidminer
  • Pfahler/2013a - Effizienteres k-means Clustering von Zeitreihen mit Dynamic Time Warping durch kaskadiertes Berechnen von unteren Schranken
  • Skirzynski/2013a - Aggregation häufiger Mengen in Datenströmen
  • Spain/2013a - A Survey on Subspace Clustering
  • Egorov/2012a - Logistic regression with group ell 1 vs. elastic net regularization
  • Kokott/2012a - Verkehrsvorhersage unter Verwendung von Methoden des maschinellen Lernens
  • Skirzynski/2012a - Subspace-Clustering mit parallelen häufigen Mengen
  • Piatkowski/2011a - Parallele Implementierung von Conditional Random Fields unter Verwendung von General-Purpose computation on Graphics Processing Units

Preliminary Work:

Lokuciejewski/etal/2010a Lokuciejewski, Paul and Stolpe, Marco and Morik, Katharina and Marwedel, Peter. Automatic Selection of Machine Learning Models for WCET-aware Compiler Heuristic Generation. In Proceedings of the 4th Workshop on Statistical and Machine Learning Approaches to ARchitecture and compilaTion (SMART), 2010.


Morik/etal/2010c Morik, Katharina and Jungermann, Felix and Piatkowski, Nico and Engel,Michael. Enhancing Ubiquitous Systems Through System Call Mining. In Large-scale Analytics for Complex Instrumented Systems (LACIS), Workshop at ICDM 2010, 2010.


Tartler/etal/2010a Reinhard Tartler and Daniel Lohmann and Fabian Scheler and Olaf Spinczyk. AspectC++: An Integrated Approach for Static and Dynamic Adaptation of System Software. In Knowledge-Based Systems, Vol. In press, Corrected Proof, 2010.


Bockermann/etal/2009a Bockermann, Christian and Apel, Martin and Meier, Michael. Learning SQL for Database Intrusion Detection Using Context-Sensitive Modelling. In Proceedings of the 6th Detection of Intrusions and Malware, and Vulnerability Assessment, pages 196 -- 205, Springer, 2009.


Lohmann/etal/2009a Daniel Lohmann and Wanja Hofer and Wolfgang Schröder-Preikschat and Jochen Streicher and Olaf Spinczyk. CiAO: An Aspect-Orientated Operating-System Family for Resource-Constrained Embedded Systems. In Proceedings of the 2009 USENIX Annual Technical Conference, pages 215--228, Berkeley, CA, USA, USENIX Association, 2009.


Lokuciejewski/etal/2009a Lokuciejewski, Paul and Gedikli, Fatih and Marwedel, Peter and Morik, Katharina. Automatic WCET Reduction by Machine Learning Based Heuristics for Function Inlining. In SMART '09: Proc. of the 3rd Workshop on Statistical and Machine Learning Approaches to Architecture and Compilation, pages 1--15, Paphos / Cyprus, 2009.


Morik/etal/2009a May, Michael and Berendt, Bettina and Cornuejols, Antoine and Gama, Joao and Giannotti, Fosca and Hotho, Andreas and Malerba, Donato and Menesalvas, Ernestina and Morik, Katharina and Pedersen, Rasmus and Saitta, Lorenza and Saygin, Yucel and Schuster, Assaf and Vanhoof, Koen. Research Challenges in Ubiquitous Knowledge Discovery. In Kargupta and Han and Yu and Motwani and Kumar (editors), Next Generation of Data Mining, pages 131--151, CRC Press, 2009.


Mierswa/etal/2008a Mierswa, Ingo and Morik, Katharina and Wurst, Michael. Handling Local Patterns in Collaborative Structuring. In Masseglia, Florent and Poncelet, Pascal and Teisseire, Maguelonne (editors), Successes and New Directions in Data Mining, pages 167 -- 186, IGI Global, 2008.


Mierswa/Morik/2008a Mierswa, Ingo and Morik, Katharina. About the Non-Convex Optimization Problem Induced by Non-positive Semidefinite Kernel Learning. In Advances in Data Analysis and Classification, Vol. 2, No. 3, pages 241--258, 2008.


Rosenmueller/etal/2008a Marko Rosenmüller and Norbert Siegmund and Horst Schirmeier and Julio Sincero and Sven Apel and Thomas Leich and Olaf Spinczyk and Gunter Saake. FAME-DBMS: Tailor-made Data Management Solutions for Embedded Systems. In Workshop on Software Engineering for Tailor-made Data Management, pages 1--6, School of Computer Science, University of Magdeburg, 2008.


Jungermann/2007a Jungermann, Felix. Named entity recognition without domain-knowledge using conditional random fields. In Workshop Notes of the Machine Learning for Natural Language Processing Workshop, pages 16 -- 17, van Someren, Marten and Katrenko, Sophia and Adriaans Pieter, 2007.


Spinczyk/Lohmann/2007a Olaf Spinczyk and Daniel Lohmann. The Design and Implementation of AspectC++. In Knowledge-Based Systems, Special Issue on Techniques to Produce Intelligent Secure Software, Vol. 20, No. 7, pages 636--651, Elsevier North-Holland, Inc., 2007.


Lohmann/etal/2006b Daniel Lohmann and Olaf Spinczyk and Wolfgang Schröder-Preikschat. Lean and Efficient System Software Product Lines: Where Aspects Beat Objects. In Awais Rashid and Mehmet Aksit (editors), Transactions on AOSD II, No. 4242, pages 227--255, Springer, 2006.