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Latest publications

  • Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl:
    „A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things“
    accepted at the 14th ACM International Conference on Distributed and Event-Based Systems (DEBS), 2020
  • Sebastian Kruse, Zoi Kaoudi, Sanjay Chawla, Felix Naumann, Bertty Contreras-Rojas, Jorge-Arnulfo Quiané-Ruiz:
    „RHEEMix in the Data Jungle: A Cost-based Optimizer for Cross-Platform Systems“
    to appear in The VLDB Journal (VLDBJ), 2020
  • Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Bertty Contreras-Rojas, Rodrigo Pardo-Meza, Anis Troudi, Sanjay Chawla:
    „ML-based Cross-Platform Query Optimization“
    IEEE 36th International Conference on Data Engineering (ICDE), 2020
  • Schütt, K.T.; Chmiela, S.; von Lilienfeld, O.A.; Tkatchenko, A.; Tsuda, K.; Müller, K.-R. (Eds.):
    „Machine Learning Meets Quantum Physics“
    Lecture Notes in Physics, Springer, 2020
  • Del Monte, Bonaventura; Zeuch, Steffen; Rabl, Tilmann; Markl, Volker:
    „Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Derakhshan, Behrouz; Mahdiraji, Alireza Rezaei; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker:
    „Optimizing Machine Learning Workloads in Collaborative Environments“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Grulich, Philipp M.; Breß, Sebastian; Zeuch, Steffen; Traub, Jonas; von Bleichert, Janis; Chen, Zongxiong; Rabl, Tilmann; Markl, Volker:
    „Grizzly: Efficient Stream Processing Through Adaptive Query Compilation“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Lutz, Clemens; Breß, Sebastian; Zeuch, Steffen; Rabl, Tilman; Markl, Volker:
    „Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Wiedemann, Simon; Kirchhoffer, Heiner; Matlage, Stefan; Haase, Paul; Marban, Arturo; Marinc, Talmaj; Neumann, David; Nguyen, Tung; Osman, Ahmed; Schwarz, Heiko; Marpe, Detlev; Wiegand, Thomas; Samek, Wojciech:
    „DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks“
    IEEE Journal of Selected Topics in Signal Processing, 14(3):1-15, 2020
  • Strodthoff, Nils; Wagner, Patrick; Wenzel, Markus; Samek, Wojciech:
    „Universal Deep Sequence Models for Protein Classification“
    Bioinformatics, btaa003, 2020
  • Samek, Wojciech:
    „Learning with Explainable Trees“
    Nature Machine Intelligence, 2:16-17, 2020
  • Sattler, Felix; Müller, Klaus-Robert; Wiegand, Thomas; Samek, Wojciech:
    „On the Byzantine Robustness of Clustered Federated Learning“
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
  • Redyuk, S.; Schelter, S.; Rukat, T.; Markl, V.; F. Biessmann, F.: „Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data“
    HILDA’19, Amsterdam, Netherlands, 2019
  • Redyuk, S.:
    „Automated Documentation of End-to-End Experiments in Data Science“
    In Ph.D. Symposium track, IEEE 35th International Conference on Data Engineering (ICDE’19), 2019
  • Karimov, Jeyhun; Rabl, Tilmann; Markl, Volker:
    AJoin: Adhoc Stream Joins at Scale
    Proceedings of the VLDB Endowment, Vol. 13, No. 4, 2019
    https://doi.org/10.14778/3372716.3372718
  • Behnke, Ilja; Thamsen, Lauritz; Kao, Odej Héctor:
    A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds
    Proceedings of the 12th {IEEE/ACM} International Conference on Utility and Cloud Computing, {UCC} 2019, pp. 15 – 20, 2019
    https://doi.org/10.1145/3368235.3368832
  • Nicoli, KA; Nakajima, S; Strodthoff, N; Samek, W; Kessel, P:
    Asymptotically Unbiased Generative Neural Sampling
    2019
    arXiv:1910.13496
  • Salem, Farouk; Schintke, Florian; Schütt, Thorsten; Reinefeld, Alexander:
    Scheduling data streams for low latency and high throughput on a Cray XC40 using Libfabric
    Concurrency and Computation Practice and Experience, pp. 1 – 14, 2019
    https://doi.org/10.1002/cpe.5563
  • Semmler, Niklas; Smaragdakis, Georgios; Feldmann, Anja:
    Distributed Mega-Datasets: The Need for Novel Computing Primitives
    39th {IEEE} International Conference on Distributed Computing Systems, 2019
    htps://doi.org/10.1109/ICDCS.2019.00167
  • Valeriani, M; Kräutli, F; Zamani, M; Tejedor, A; Sander, C; Vogl, M; Bertram, S; Funke, G; Kantz, H:
    The Emergence of Epistemic Communities in the Sphaera Corpus
    Journal of Historical Network Research, 3 (1), pp. 50-91, 2019
    https://doi.org/10.25517/jhnr.v3i1.63
  • Vidaurre C; Nolte, G; de Vries, IEJ; Gómez, M; Boonstra, TW; Müller, KR; Villringer, A; Nikulin, VV:
    Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets
    NeuroImage, 201, 2019
    https://doi.org/10.1016/j.neuroimage.2019.116009
  • von Lühmann, A; Boukouvalas, Z; Müller, KR; Adalı, T:
    A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
    NeuroImage, 200, pp. 72-88, 2019
    https://doi.org/10.1016/j.neuroimage.2019.06.021
  • Mahdavi, Mohammad; Neutatz, Felix; Visengeriyeva, Larysa; Abedjan, Ziawasch:
    Towards Automated Data Cleaning Workflows
    Proceedings of the Conference on „Lernen, Wissen, Daten, Analysen“ (LWDA2019), pp. 10-19, 2019
    http://ceur-ws.org/Vol-2454/paper_8.pdf
  • Vidaurre C; Murguialday, AR; Haufe, S; Gómez, M; Müller, KR; Nikulin, VV:
    Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation
    NeuroImage, 199, pp. 375-386, 2019
    https://doi.org/10.1016/j.neuroimage.2019.05.074
  • Sauceda, HE; Chmiela, S; Poltavsky, I; Müller, KR; Tkatchenko, A:
    Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
    2019
    arXiv:1909.08565
  • Schütt, KT; Gastegger, M; Tkatchenko, A; Müller, KR:
    Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, p. 311-330, Springer International Publishing, 2019,
    https://doi.org/10.1007/978-3-030-28954-6_17
  • Samek, W; Müller, KR:
    Towards Explainable Artificial Intelligence
    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 5-22, Springer International Publishing, 2019
    https://doi.org/10.1007/978-3-030-28954-6_1
  • Iravani, S; Conrad, TOF:
    Deep Learning for Proteomics Data for Feature Selection and Classification
    Machine Learning and Knowledge Extraction, pp. 301-316, Springer International Publishing, 2019
    https://doi.org/10.1007/978-3-030-29726-8_19
  • Hägele, M; Seegerer, P; Lapuschkin, S; Bockmayr, M; Samek, W; Klauschen, F; Müller, KR; Binder, A:
    Resolving challenges in deep learning-basedanalyses of histopathological images usingexplanation methods
    2019
    https://arxiv.org/abs/1908.06943
  • Alber, M; Lapuschkin, S; Seegerer, P; Hägele, M; Schütt, KT; Montavon, G; Samek, W; Müller, KR; Dähne, S; Kindermans, PJ:
    iNNvestigate neural networks!
    2019
    https://arxiv.org/abs/1808.04260
  • Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Ziehn, Ariane; Rabl, Tilmann; Markl, Volker:
    Particulate Matter Matters—The Data Science Challenge @ BTW 2019
    Datenbank-Spektrum, Vol. 19, Issue 3, pp. 165-182, 2019 https://doi.org/10.1007/s13222-019-00322-x
  • Kunft, Andreas; Katsifodimos, Asterios; Schelter, Sebastian; Breß, Sebastian; Rabl, Tilmann; Markl, Volker:
    An Intermediate Representation for Optimizing Machine Learning Pipelines
    Proceedings of the VLDB Endowment, Vol. 12, No. 11, 2019
    http://www.vldb.org/pvldb/vol12/p1553-kunft.pdf
  • Bosse, S; Becker, S; Müller, KR; Samek, W; Wiegand, T:
    Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network
    Digital Signal Processing, 91, pp. 54-65, 2019.
    https://doi.org/10.1016/j.dsp.2018.12.005
  • Karimov, Jeyhun; Rabl, Tilmann; Markl, Volker:
    AStream: Ad-hoc Shared Stream Processing
    SIGMOD ’19: Proceedings of the 2019 International Conference on Management of Data, pp. 607–622, 2019
    https://doi.org/10.1145/3299869.3319884
  • Traub, Jonas; Grulich, Philipp; Cuéllar, Alejandro Rodríguez; Breß, Sebastian; Katsifodimos, Asterios; Rabl, Tilmann; Markl, Volker:
    Efficient Window Aggregation with General Stream Slicing
    22th International Conference on Extending Database Technology (EDBT), 2019
    https://dx.doi.org/10.5441/002/edbt.2019.10
  • Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker:
    Explanation of Air Pollution Using External Data Sources
    Datenbanksysteme für Business, Technologie und Web (BTW 2019), pp. 297-300, 2019
    https://dl.gi.de/handle/20.500.12116/21820
  • Giouroukis, Dimitrios; Hülsmann, Julius; Bleichert, Janis von; Geldenhuys, Morgan; Stullich, Tim; Gutierrez, Felipe Oliveira; Traub, Jonas; Beedkar, Kaustubh; Markl, Volker:
    Resense: Transparent Record and Replay of Sensor Data in the Internet of Things
    Proceedings of the 21st International Conference on Extending Database Technology (EDBT), 2019
    https://doi.org/10.5441/002/edbt.2019.63