Forschungsbereiche

Schwerpunkt unserer Forschung sind Techniken zur Verwaltung und Analyse großer Datenbestände. Gegenstand unserer Arbeit ist die Synthese konzeptioneller Grundlagenforschung mit dem prototypischen Einsatz in unterschiedlichen Anwendungen. Zu unseren derzeitigen Themen gehören insbesondere die effiziente Erkennung von unerwarteten und statistisch auffälligen Datenobjekten, Techniken, die unterschiedliche personenbezogene Informationen in großen Datenbeständen möglichst gut verstecken, intelligente Verarbeitung von Daten aus Sensornetzen und die Entwicklung von Technologie zur Modellierung und Ausführung sicherer und regelkonformer Abläufe mit Fokus auf den Datenaspekt. Wir sind dabei stets sehr interessiert an der Zusammenarbeit mit Anwendern und mit Forschern aus anderen Wissenschaftsdisziplinen.

Publikationen


2024
A graph database for feature characterization of dislocation networks
Katzer, B.; Betsche, D.; Böhm, K.; Weygand, D.; Schulz, K.
2024. Scripta Materialia, 240, Art.-Nr.: 115841. doi:10.1016/j.scriptamat.2023.115841 VolltextVolltext der Publikation als PDF-Dokument
Adaptive Bernstein change detector for high-dimensional data streams
Heyden, M.; Fouché, E.; Arzamasov, V.; Fenn, T.; Kalinke, F.; Böhm, K.
2024. Data Mining and Knowledge Discovery, 38 (3), 1334–1363. doi:10.1007/s10618-023-00999-5 VolltextVolltext der Publikation als PDF-Dokument
Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning
Bielski, P.; Witterauf, L.; Jendral, S.; Mikut, R.; Bach, J.
2024. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Ed.: D. Aveiro. Vol. 2, 216–226, SciTePress. doi:10.5220/0013065900003838
Towards a Temporal Graph Query Language for Durable Patterns
Betsche, D.; Katzer, B.; Schulz, K.; Böhm, K.
2024. SSDBM ’24: Proceedings of the 36th International Conference on Scientific and Statistical Database Management, Rennes, 10th-112th July 2024, 1–4, Association for Computing Machinery (ACM). doi:10.1145/3676288.3676303 VolltextVolltext der Publikation als PDF-Dokument
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Kalinke, F.; Szabo, Z.
2024. arxiv. doi:10.48550/arXiv.2403.07735
Nyström Kernel Stein Discrepancy
Kalinke, F.; Szabo, Z.; Skriperumbudur, B. K.
2024. arxiv. doi:10.48550/arXiv.2406.08401
Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines
Bielski, P.; Eismont, A.; Bach, J.; Leiser, F.; Kottonau, D.; Böhm, K.
2024. 15th ACM International Conference on Future and Sustainable Energy Systems, Singapur, 4th-7th June 2024, 279–290, Association for Computing Machinery (ACM). doi:10.1145/3632775.3661967 VolltextVolltext der Publikation als PDF-Dokument
Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations
Katzer, B.; Betsche, D.; von Hoegen, F.; Jochum, B.; Böhm, K.; Schulz, K.
2024. Modelling and Simulation in Materials Science and Engineering, 32 (5), Art.-Nr.: 055026. doi:10.1088/1361-651X/ad4b4c VolltextVolltext der Publikation als PDF-Dokument
DEAL: Data-Efficient Active Learning for Regression Under Drift
Böhnke, B. H.; Fouché, E.; Böhm, K.
2024. Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part VI. Ed.: D.-N. Yang, 188 – 200, Springer Nature Singapore. doi:10.1007/978-981-97-2266-2_15
Using Constraints to Discover Sparse and Alternative Subgroup Descriptions
Bach, J.
2024. arxiv. doi:10.48550/arXiv.2406.01411 VolltextVolltext der Publikation als PDF-Dokument
Alternative feature selection with user control
Bach, J.; Böhm, K.
2024. International Journal of Data Science and Analytics. doi:10.1007/s41060-024-00527-8 VolltextVolltext der Publikation als PDF-Dokument
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading
Dinh, T. A.; Mullov, C.; Bärmann, L.; Li, Z.; Liu, D.; Reiß, S.; Lee, J.; Lerzer, N.; Gao, J.; Peller-Konrad, F.; Röddiger, T.; Waibel, A.; Asfour, T.; Beigl, M.; Stiefelhagen, R.; Dachsbacher, C.; Böhm, K.; Niehues, J.
2024. Y. Al-Onaizan, M. Bansal & Y.-N. Chen (Hrsg.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, 12th-16th November 2024, Hrsg.: Al-Onaizan, Y., Bansal, M., Chen, Y.-N., 11592–11610, Association for Computational Linguistics (ACL) VolltextVolltext der Publikation als PDF-Dokument
Experimental Data for the Dissertation "Leveraging Constraints for User-Centric Feature Selection"
Bach, J.
2024, November 8. doi:10.35097/4kjyeg0z2bxmr6eh
Experimental Data for the Paper "Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning"
Bielski, P.; Witterauf, L.
2024, Oktober 7. doi:10.35097/zv8zqgqd6ezm02vk
Experimental Data for the Paper "Using Constraints to Discover Sparse and Alternative Subgroup Descriptions"
Bach, J.
2024, Juni 3. doi:10.35097/caKKJCtoKqgxyvqG
Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"
Bielski, P.; Kottonau, D.
2024, April 26. doi:10.35097/sLJiahifxvfDKMEc
Experimental Data for the Paper "Alternative feature selection with user control"
Bach, J.
2024, März 21. doi:10.35097/1975
Experimental Data for the Paper "Finding Optimal Diverse Feature Sets with Alternative Feature Selection" (Version 2)
Bach, J.
2024, Februar 13. doi:10.35097/1920
2023
Nyström -Hilbert-Schmidt Independence Criterion
Kalinke, F.; Szabó, Z.
2023. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2023, 1005 – 1015, Machine Learning Research Press (ML Research Press)
Active Learning for SAT Solver Benchmarking
Fuchs, T.; Bach, J.; Iser, M.
2023. Tools and Algorithms for the Construction and Analysis of Systems. Ed.: S. Sankaranarayanan. Pt. 1, 407–425, Springer Nature Switzerland. doi:10.1007/978-3-031-30823-9_21 VolltextVolltext der Publikation als PDF-Dokument
Adaptive Bernstein change detector for high-dimensional data streams
Heyden, M.; Fouché, E.; Arzamasov, V.; Fenn, T.; Kalinke, F.; Böhm, K.
2023. doi:10.48550/arXiv.2306.12974 VolltextVolltext der Publikation als PDF-Dokument
Multi-kernel Times Series Outlier Detection
Kalinke, F.; Fouché, E.; Thiessen, H.; Böhm, K.
2023. Discovery Science. DS 2023. Eds.: A. Bifet, A.C. Lorena, R.P. Ribeiro, J. Gama, P.H. Abreu, 688 – 702, Springer Nature Switzerland. doi:10.1007/978-3-031-45275-8_46
Nyström -Hilbert-Schmidt Independence Criterion
Kalinke, F.; Szabó, Z.
2023. doi:10.48550/arXiv.2302.09930
Finding Optimal Diverse Feature Sets with Alternative Feature Selection
Bach, J.
2023. arxiv. doi:10.48550/arXiv.2307.11607 VolltextVolltext der Publikation als PDF-Dokument
CH-Bench: a user-oriented benchmark for systems for efficient distant reading (design, performance, and insights)
Willkomm, J.; Raster, M.; Schäler, M.; Böhm, K.
2023. International Journal on Digital Libraries, 24 (4), 243–261. doi:10.1007/s00799-023-00347-4 VolltextVolltext der Publikation als PDF-Dokument
Reduction of data-value-aware process models: A relevance-based approach
Ordoni, E.; Mülle, J.; Böhm, K.
2023. Information Systems, 114, Art.Nr. 102157. doi:10.1016/j.is.2022.102157
A benchmark of categorical encoders for binary classification
Matteucci, F.; Arzamasov, V.; Boehm, K.
2023. arxiv VolltextVolltext der Publikation als PDF-Dokument
A benchmark of categorical encoders for binary classification
Matteucci, F.; Arzamasov, V.; Böhm, K.
2023. A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt & S. Levine (Hrsg.), Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 Hrsg.: Oh, Alice; Naumann, Tristan; Globerson, Amir; Saenko, Kate; Hardt, Moritz; Levine, Sergey, PMLR
Experimental Data for the Paper "Finding Optimal Diverse Feature Sets with Alternative Feature Selection"
Bach, J.
2023, Juli 13. doi:10.35097/1623
2022
Analyzing and Predicting Verification of Data-Aware Process Models – a Case Study with Spectrum Auctions
Ordoni, E.; Bach, J.; Fleck, A.
2022. IEEE Access, 10, 31699–31713. doi:10.1109/ACCESS.2022.3154445 VolltextVolltext der Publikation als PDF-Dokument
Efficient SVDD sampling with approximation guarantees for the decision boundary
Englhardt, A.; Trittenbach, H.; Kottke, D.; Sick, B.; Böhm, K.
2022. Machine Learning, 111 (4), 1349–1375. doi:10.1007/s10994-022-06149-0 VolltextVolltext der Publikation als PDF-Dokument
Swellfish privacy: Supporting time-dependent relevance for continuous differential privacy
Tex, C.; Schäler, M.; Böhm, K.
2022. Information Systems, 109, Art.-Nr.: 102079. doi:10.1016/j.is.2022.102079
Tandem Outlier Detectors for Decentralized Data
Heyden, M.; Wilwer, J.; Fouché, E.; Thoma, S.; Matthiesen, S.; Gwosch, T.
2022. Proceedings of the 34th International Conference on Scientific and Statistical Database Management, Article: 25, Association for Computing Machinery (ACM). doi:10.1145/3538712.3538748 VolltextVolltext der Publikation als PDF-Dokument
An Empirical Evaluation of Constrained Feature Selection
Bach, J.; Zoller, K.; Trittenbach, H.; Schulz, K.; Böhm, K.
2022. SN Computer Science, 3 (6), Art.-Nr.: 445. doi:10.1007/s42979-022-01338-z VolltextVolltext der Publikation als PDF-Dokument
Establishing trajectories of moving objects without identities: The intricacies of cell tracking and a solution
Cazzolato, M. T.; Traina, A. J. M.; Böhm, K.
2022. Information Systems, 105, Art.-Nr.: 101955. doi:10.1016/j.is.2021.101955
Results from the Verification of Models of Spectrum Auctions
Ordoni, E.; Mülle, J.; Böhm, K.
2022. Business Modeling and Software Design – 12th International Symposium, BMSD 2022, Fribourg, Switzerland, June 27–29, 2022, Proceedings, 54–68, Springer International Publishing. doi:10.1007/978-3-031-11510-3_4
Efficient SVDD sampling with approximation guarantees for the decision boundary
Englhardt, A.; Trittenbach, H.; Kottke, D.; Sick, B.; Böhm, K.
2022. doi:10.48550/arXiv.2009.13853
Leveraging Constraints for User-Centric Selection of Predictive Features
Bach, J.
2022, Oktober 6. AI Hub @ Karlsruhe (2022), Karlsruhe, Deutschland, 5.–7. Oktober 2022 VolltextVolltext der Publikation als PDF-Dokument
Presentation for the Paper "A Comprehensive Study of k-Portfolios of Recent SAT Solvers"
Bach, J.
2022, August 2. 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022), Haifa, Israel, 2.–5. August 2022 VolltextVolltext der Publikation als PDF-Dokument
Experimental Data for the Paper ’’An Empirical Evaluation of Constrained Feature Selection"
Bach, J.; Zoller, K.; Schulz, K.
2022, Juli 26. doi:10.5445/IR/1000148891
Experimental Data for the Paper "A Comprehensive Study of k-Portfolios of Recent SAT Solvers"
Bach, J.; Iser, M.
2022, Mai 30. doi:10.5445/IR/1000146629
Experimental data for the paper "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions"
Ordoni, E.; Bach, J.; Fleck, A.-K.
2022, Februar 14. doi:10.5445/IR/1000142949
2021
An overview and a benchmark of active learning for outlier detection with one-class classifiers
Trittenbach, H.; Englhardt, A.; Böhm, K.
2021. Expert systems with applications, 168, Art. Nr.: 114372. doi:10.1016/j.eswa.2020.114372
An Evaluation of NILM Approaches on Industrial Energy-Consumption Data
Kalinke, F.; Bielski, P.; Singh, S.; Fouché, E.; Böhm, K.
2021. e-Energy ’21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, 239–243, Association for Computing Machinery (ACM). doi:10.1145/3447555.3464863
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data
Steinbuss, G.; Böhm, K.
2021. ACM Transactions on Knowledge Discovery from Data, 15 (4), Art.-Nr.: 3441453. doi:10.1145/3441453
Generating artificial outliers in the absence of genuine ones - A survey
Steinbuss, G.; Böhm, K.
2021. ACM Transactions on Knowledge Discovery from Data, 15 (2), Art.-Nr.: 30. doi:10.1145/3447822
How meaningful are similarities in deep trajectory representations?
Taghizadeh, S.; Elekes, A.; Schäler, M.; Böhm, K.
2021. Information systems, 98, Article: 101452. doi:10.1016/j.is.2019.101452
Efficient subspace search in data streams
Fouché, E.; Kalinke, F.; Böhm, K.
2021. Information systems, 97, Art.-Nr. 101705. doi:10.1016/j.is.2020.101705
Preserving secrecy in mobile social networks
Suntaxi, G.; El Ghazi, A. A.; Böhm, K.
2021. ACM Transactions on Cyber-Physical Systems, 5 (1), 5. doi:10.1145/3396071
Scalable and data-aware SQL query recommendations
Arzamasova, N.; Böhm, K.
2021. Information systems, 96, Art.-Nr.: 101646. doi:10.1016/j.is.2020.101646
A framework for dependency estimation in heterogeneous data streams
Fouché, E.; Mazankiewicz, A.; Kalinke, F.; Böhm, K.
2021. Distributed and parallel databases, 39, 415–444. doi:10.1007/s10619-020-07295-x VolltextVolltext der Publikation als PDF-Dokument
Secrecy and performance models for query processing on outsourced graph data
Suntaxi, G.; El Ghazi, A. A.; Böhm, K.
2021. Distributed and parallel databases, 39, 35–77. doi:10.1007/s10619-020-07284-0 VolltextVolltext der Publikation als PDF-Dokument
REDS: Rule Extraction for Discovering Scenarios
Arzamasov, V.; Böhm, K.
2021. SIGMOD/PODS ’21: Proceedings of the 2021 International Conference on Management of Data: June 20-25, 2021, Virtual Event China, 115–128, Association for Computing Machinery (ACM). doi:10.1145/3448016.3457301
An ensemble technique for better decisions based on data streams and its application to data privacy
Laforet, F.; Olms, C.; Biczok, R.; Böhm, K.
2021. IEEE Transactions on Knowledge and Data Engineering, 33 (12), 3662–3674. doi:10.1109/TKDE.2020.2977035
Accurate Cardinality Estimation of Co-occurring Words Using Suffix Trees
Willkomm, J.; Schäler, M.; Böhm, K.
2021. Database Systems for Advanced Applications – 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part II. Ed.: C. Jensen, 721–737, Springer International Publishing. doi:10.1007/978-3-030-73197-7_50
Comprehensible and Robust Knowledge Discovery from Small Datasets. Dissertation
Arzamasov, V.
2021, Juli 2. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000134366 VolltextVolltext der Publikation als PDF-Dokument
Accurate Cardinality Estimation of Co-occurring Words Using Suffix Trees (Extended Version)
Willkomm, J.; Schäler, M.; Böhm, K.
2021. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000128678 VolltextVolltext der Publikation als PDF-Dokument
2020
Data-driven exploration and continuum modeling of dislocation networks
Sudmanns, M.; Bach, J.; Weygand, D.; Schulz, K.
2020. Modelling and simulation in materials science and engineering, 28 (6), Art. Nr.: 065001. doi:10.1088/1361-651X/ab97ef VolltextVolltext der Publikation als PDF-Dokument
On the Usefulness of SQL-Query-Similarity Measures to Find User Interests
Arzamasova, N.; Bohm, K.; Goldman, B.; Saaler, C.; Schaler, M.
2020. IEEE transactions on knowledge and data engineering, 32 (10), 1982–1999. doi:10.1109/TKDE.2019.2913381 VolltextVolltext der Publikation als PDF-Dokument
Active Learning of SVDD Hyperparameter Values
Trittenbach, H.; Böhm, K.; Assent, I.
2020. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics: 6-9 Ocotber 2020, Sydney, Australia. Ed.: G. Webb, 109–117, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA49011.2020.00023
Finding the sweet spot: Batch selection for one-class active learning
Englhardt, A.; Trittenbach, H.; Vetter, D.; Böhm, K.
2020. Proceedings of the 2020 SIAM International Conference on Data Mining. Ed.: C. Demeniconi, 118–126, SIAM. doi:10.1137/1.9781611976236.14
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines
Geiger, C.; Maier, N.; Kalinke, F.; Geimer, M.
2020. Fluid Power Future Technology! Vol. 3, 83–92, Technische Universität Dresden (TU Dresden). doi:10.25368/2020.97 VolltextVolltext der Publikation als PDF-Dokument
Improving semantic change analysis by combining word embeddings and word frequencies
Englhardt, A.; Willkomm, J.; Schäler, M.; Böhm, K.
2020. International journal on digital libraries, 21 (3), 247–264. doi:10.1007/s00799-019-00271-6
Privacy Measures and Storage Technologies for Battery-Based Load Hiding - an Overview and Experimental Study
Arzamasov, V.; Schwerdt, R.; Karrari, S.; Böhm, K.; Nguyen, T. B.
2020. e-Energy ’20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems, Melbourne, Australia, 22 - 26 June 2020, 178–195, Association for Computing Machinery (ACM). doi:10.1145/3396851.3398320
Mining text outliers in document directories
Fouché, E.; Meng, Y.; Guo, F.; Zhuang, H.; Böhm, K.; Han, J.
2020. Proceedings 20th IEEE International Conference on Data Mining: 17-20 November 2020, Virtual Conference. Ed.: C. Plant, 152–161, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICDM50108.2020.00024
Verification of Data-Value-Aware Processes and a Case Study on Spectrum Auctions
Ordoni, E.; Mulle, J.; Bohm, K.
2020. 2020 IEEE 22nd Conference on Business Informatics (CBI), 22 - 24 June 2020, Antwerp, Belgium, Belgium, 181–190, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CBI49978.2020.00027
A Data-driven Approach for Estimating Relative Voltage Sensitivity
Karrari, S.; Vollmer, M.; Carne, G.; Noe, M.; Böhm, K.; Geisbüsch, J.
2020. IEEE Power & Energy Society General Meeting (PESGM 2020), 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PESGM41954.2020.9281859 VolltextVolltext der Publikation als PDF-Dokument
Exploring the unknown – Query synthesis in one-class active learning
Englhardt, A.; Böhm, K.
2020. Proceedings of the 2020 SIAM International Conference on Data Mining. Ed.: C. Demeniconi, 145–153, SIAM. doi:10.1137/1.9781611976236.17
Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams. Dissertation
Fouché, E.
2020, Dezember 8. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000127232 VolltextVolltext der Publikation als PDF-Dokument
Swellfish Privacy: Exploiting Time-Dependent Relevance for Continuous Differential Privacy : Technical Report
Tex, C.; Schäler, M.; Böhm, K.
2020. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000123707 VolltextVolltext der Publikation als PDF-Dokument
Data-driven exploration and continuum modeling of dislocation networks
Sudmanns, M.; Bach, J.; Weygand, D.; Schulz, K.
2020. doi:10.5445/IR/1000119138 VolltextVolltext der Publikation als PDF-Dokument
2019
Scaling multi-armed bandit algorithms
Fouché, E.; Komiyama, J.; Böhm, K.
2019. 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019; Anchorage; United States; 4 August 2019 through 8 August 2019, 1449–1459, Association for Computing Machinery (ACM). doi:10.1145/3292500.3330862
FOBSS: Monitoring data from a modular battery system
Steinbuss, G.; Rzepka, B.; Bischof, S.; Blank, T.; Böhm, K.
2019. Proceedings of the Tenth ACM International Conference on Future Energy Systems - e-Energy ’19, Phoenix, AZ, USA, June 25 - 28, 2019, 456–459, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331020
Reducing energy time series for energy system models via self-organizing maps
Yilmaz, H. Ü.; Fouché, E.; Dengiz, T.; Krauß, L.; Keles, D.; Fichtner, W.
2019. Information technology, 61 (2-3), 125–133. doi:10.1515/itit-2019-0025
A practical data-flow verification scheme for business processes
Mülle, J.; Tex, C.; Böhm, K.
2019. Information systems, 81, 136–151. doi:10.1016/j.is.2018.12.002
One-class active learning for outlier detection with multiple subspaces
Trittenbach, H.; Böhm, K.
2019. CIKM ’19 Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, November 3-7, 2019, 811–820, Association for Computing Machinery (ACM). doi:10.1145/3357384.3357873
Learning from few samples: Lexical substitution with word embeddings for short text classification
Elekes, Á.; Di Stefano, A. S.; Schäler, M.; Böhm, K.; Keller, M.
2019. 19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019; Urbana-Champaign; United States; 2 June 2019 through 6 June 2019. Ed.: M. Bonn, 111–119, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/JCDL.2019.00025
Unsupervised Artificial Neural Networks for Outlier Detection in High-Dimensional Data
Popovic, D.; Fouché, E.; Böhm, K.
2019. Advances in Databases and Information Systems: 23rd European Conference, ADBIS 2019, Bled, Slovenia, September 8–11, 2019, Proceedings. Ed.: T. Welzer, 3–19, Springer. doi:10.1007/978-3-030-28730-6_1
Validating one-class active learning with user studies – A prototype and open challenges
Trittenbach, H.; Englhardt, A.; Böhm, K.
2019. IAL 2019 Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany, September 16th, 2019. Ed.: D. Kottke, 17–31 VolltextVolltext der Publikation als PDF-Dokument
Efficient Interval-focused Similarity Search under Dynamic Time Warping
Willkomm, J.; Bettinger, J.; Schäler, M.; Böhm, K.
2019. Proceedings of the 16th International Symposium on Spatial and Temporal Databases - SSTD ’19, 130–139, Association for Computing Machinery (ACM). doi:10.1145/3340964.3340969
The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving
Werle, D.; Warzel, D.; Bischof, S.; Koziolek, A.; Trittenbach, H.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 482–485, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331023
Iterative estimation of mutual information with error bounds
Vollmer, M.; Böhm, K.
2019. 22nd International Conference on Extending Database Technology, EDBT 2019; Lisbon; Portugal; 26 March 2019 through 29 March 2019. Ed.: Z. Kaoudi, 73–84, OpenProceedings.org. doi:10.5441/002/edbt.2019.08 VolltextVolltext der Publikation als PDF-Dokument
Understanding the effects of temporal energy-data aggregation on clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2019. Information technology, 61 (2-3), 111–123. doi:10.1515/itit-2019-0014
Data-driven crack assessment based on surface measurements
Schulz, K.; Kreis, S.; Trittenbach, H.; Böhm, K.
2019. Engineering fracture mechanics, 218, Article no: 106552. doi:10.1016/j.engfracmech.2019.106552
Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions
Vollmer, M.; Englhardt, A.; Trittenbach, H.; Bielski, P.; Karrari, S.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 474–481, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331022
High-resolution Industrial Production Energy (HIPE) | Version 1.0.1
Bischof, S.; Trittenbach, H.; Werle, D.; Blank, T.; Böhm, K.
2019, Mai 1. doi:10.5281/zenodo.14054902
Active Learning of SVDD Hyperparameter Values
Trittenbach, H.; Böhm, K.; Assent, I.
2019
On Preserving Secrecy in Mobile Social Networks
Suntaxi, G.; El Ghazi, A. A.; Böhm, K.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000096955/v2 VolltextVolltext der Publikation als PDF-Dokument
Frequent Observations from a Battery System with Subunits
Steinbuß, G.; Rzepka, B.; Bischof, S.; Blank, T.; Böhm, K.
2019. doi:10.5445/IR/1000094469
Verifying Workflow Models with Data Values - a Case Study of SMR Spectrum Auctions
Ordoni, E.; Mülle, J.; Böhm, K.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000093998 VolltextVolltext der Publikation als PDF-Dokument
On Mutual Authorizations: Semantics, Integration Issues, and Performance
Suntaxi, G.; El Ghazi, A. A.; Böhm, K.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000093936 VolltextVolltext der Publikation als PDF-Dokument
On the Usefulness of SQL-Query-Similarity Measures to Find User Interests
Arzamasova, N.; Böhm, K.; Goldman, B.; Saaler, C.; Schäler, M.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000093761 VolltextVolltext der Publikation als PDF-Dokument
Minimizing Bias in Estimation of Mutual Information from Data Streams
Arzamasov, V.; Böhm, K.; Rutter, I.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000091969 VolltextVolltext der Publikation als PDF-Dokument
2018
Towards Concise Models of Grid Stability
Arzamasov, V.; Böhm, K.; Jochem, P. E. P.
2018. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018; Aalborg; Denmark; 29 October 2018 through 31 October 2018, Art. Nr.: 8587498, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SmartGridComm.2018.8587498
HIPE – An energy-Status-Data set from industrial production
Bischof, S.; Trittenbach, H.; Vollmer, M.; Werle, D.; Blank, T.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 599–603, Association for Computing Machinery (ACM). doi:10.1145/3208903.3210278
Cleaning Antipatterns in an SQL Query Log
Arzamasova, N.; Schaler, M.; Bohm, K.
2018. IEEE transactions on knowledge and data engineering, 30 (3), 421–434. doi:10.1109/TKDE.2017.2772252
Dimension-based subspace search for outlier detection
Trittenbach, H.; Böhm, K.
2018. International Journal of Data Science and Analytics. doi:10.1007/s41060-018-0137-7
Towards Meaningful Distance-preserving Encryption
Tex, C.; Schäler, M.; Böhm, K.
2018. 30th International Conference on Scientific and Statistical Database Management (SSDBM), Bozen-Bolzano, Italy, July 9 - 11, 2018, Artikel-Nr.: 2/1–12, Association for Computing Machinery (ACM). doi:10.1145/3221269.3223029
In-database analytics with ibmdbpy
Fouché, E.; Eckert, A.; Böhm, K.
2018. 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018; Bolzano-Bozen; Italy; 9 July 2018 through 11 July 2018. Ed.: M. Bohlen, Art. Nr.: 31, Association for Computing Machinery (ACM). doi:10.1145/3221269.3223026
PrivEnergy – A privacy operator framework addressing individual concerns
Tex, C.; Hertweck, P.; Schäler, M.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 426–428, Association for Computing Machinery (ACM). doi:10.1145/3208903.3212048
On the tradeoff between energy data aggregation and clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 399–401, Association for Computing Machinery (ACM). doi:10.1145/3208903.3212038
Distance-based data mining over encrypted data
Tex, C.; Schäler, M.; Böhm, K.
2018. Proceedings of the 34th IEEE International Conference on Data Engineering, Paris, F, 16-19 avril 2018, 1264–1267, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICDE.2018.00126 VolltextVolltext der Publikation als PDF-Dokument
Towards Simulation-Data Science : A Case Study on Material Failures
Trittenbach, H.; Gauch, M.; Böhm, K.; Schulz, K.
2018. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 1-3 Oct. 2018, 450–459, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA.2018.00058
Efficient and Reliable Estimation of Cell Positions
Cazzolato, M. T.; Traina, A. J. M.; Böhm, K.
2018. 27th ACM International Conference on Information and Knowledge Management, CIKM 2018; Torino; Italy; 22 October 2018 through 26 October 2018. Ed.: N. Paton, 1043–1052, Association for Computing Machinery (ACM). doi:10.1145/3269206.3271734
A Query Algebra for Temporal Text Corpora
Willkomm, J.; Schmidt-Petri, C.; Schäler, M.; Schefczyk, M.; Böhm, K.
2018. Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, Fort Worth, TX, June 3-7, 2018, 183–192, Association for Computing Machinery (ACM). doi:10.1145/3197026.3197044
High-resolution Industrial Production Energy (HIPE) | Version 1.0.0
Bischof, S.; Trittenbach, H.; Werle, D.; Blank, T.; Böhm, K.
2018, Juni 12. doi:10.5281/zenodo.14054775
Beweisbare Privatheitsgarantien durch den Einsatz wiederaufladbarer Energiespeicher
Laforet, F.; Buchmann, E.; Böhm, K.
2018. Ideen und Innovationen für die Energie von morgen : Wissenschaftliche Beiträge des KIT zu den Jahrestagungen 2014, 2016 und 2017 des KIT-Zentrums Energie. Hrsg.: W. Breh, 43–44, KIT Scientific Publishing. doi:10.5445/IR/1000085362 VolltextVolltext der Publikation als PDF-Dokument
An Overview and a Benchmark of Active Learning for One-Class Classification
Trittenbach, H.; Englhardt, A.; Böhm, K.
2018. arXiv preprint 1808.04759
On the Tradeoff between Energy Data Aggregation and Clustering Quality
Trittenbach, H.; Bach, J.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems (e-Energy 2018), Karlsruhe, Deutschland, 12.–15. Juni 2018 VolltextVolltext der Publikation als PDF-Dokument
HIPE -- An Energy-Status-Data Set from Industrial Production
Bischof, S.; Trittenbach, H.; Vollmer, M.; Werle, D.; Blank, T.; Böhm, K.
2018. International Workshop on Energy Data and Analytics (EDA 2018), Karlsruhe, Deutschland, 12. Juni 2018
Comparing Predictions of Object Movements
Taghizadeh, S.; Schäler, M.; Böhm, K.
2018. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000081005 VolltextVolltext der Publikation als PDF-Dokument
Towards Simulation-Data Science : A Case Study on Material Failures
Trittenbach, H.; Gauch, M.; Böhm, K.; Schulz, K.
2018. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000079420 VolltextVolltext der Publikation als PDF-Dokument