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DS - BI
University of Groningen
Nijenborgh 9
9747 AG Groningen
The Netherlands

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2022

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  1. Haghshenas, K., Setz, B., & Aiello, M. (2022, December). CO2 Emission Aware Scheduling for Deep Neural Network Training Workloads. 2022 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData55660.2022.10020544
  2. Lotfian Delouee, M., Koldehofe, B., & Degeler, V. (2022, December 14). Towards adaptive quality-aware Complex Event Processing in the Internet of Things. Proceedings of the 18th International Conference on Mobility, Sensing and Networking (MSN 2022). https://doi.org/10.1109/MSN57253.2022.00095
  3. Saleh, S., & Koldehofe, B. (2022). On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions. IEEE Access, 10, 129279–129312. https://doi.org/10.1109/ACCESS.2022.3226447
  4. Saleh, S., Goossens, A. S., Banerjee, T., & Koldehofe, B. (2022, December). TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing. Proceedings of the 7th International Conference on Rebooting Computing (ICRC 2022). https://doi.org/10.1109/ICRC57508.2022.00013
  5. Sadhu, K., Haghshenas, K., Rouhani, M., & Aiello, M. (2022). Optimal Joint Operation of Coupled Transportation and Power Distribution Urban Networks. Energy Informatics. https://energyinformatics.springeropen.com/articles/10.1186/s42162-022-00249-w
  6. Medema, M., & Lazovik, A. (2022). Correlating the Community Structure of Constraint Satisfaction Problems with Search Time. International Journal on Artificial Intelligence Tools, 31(07). https://doi.org/10.1142/S0218213022600041
  7. Saleh, S., Goossens, A. S., Banerjee, T., & Koldehofe, B. (2022, November). Using Memristors for Energy Efficient Cognitive Network Functions. Symposium on Physics of Information in Matter [Poster Session]. https://research.rug.nl/en/publications/using-memristors-for-energy-efficient-cognitive-network-functions
  8. Saleh, S., Goossens, A. S., Banerjee, T., & Koldehofe, B. (2022, October). Towards Energy Efficient Memristor-based TCAM for Match-Action Processing. Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC 2022). https://doi.org/10.1109/IGSC55832.2022.9969354
  9. Saleh, S., Goossens, A. S., Banerjee, T., & Koldehofe, B. (2022, October). In-Network Computing Over Memristor-Based Cognitive Network Functions. Brain-Inspired Concepts and Materials for Information Processing (Brainspiration) Conference [Poster Session]. https://research.rug.nl/en/publications/in-network-computing-over-memristor-based-cognitive-network-funct
  10. Groefsema, H., van Beest, N. R. T. P., & Governatori, G. (2022). On the Use of the Conformance and Compliance Keywords During Verification of Business Processes. In C. Di Ciccio, R. Dijkman, A. del Río Ortega, & S. Rinderle-Ma (Eds.), Business Process Management Forum (pp. 21–37). Springer. https://doi.org/10.1007/978-3-031-16171-1_2
  11. Saleh, S., & Koldehofe, B. (2022, September). Memristor-Based Cognitive Network Packet Processors. Neuromorphic Computing Netherlands (NCN 2022) Workshop [Abstracts, Talks and Posters]. https://research.rug.nl/en/publications/memristor-based-cognitive-network-packet-processors
  12. Saleh, S., Goossens, A. S., Banerjee, T., & Koldehofe, B. (2022, September). Memristor-Based Cognitive and Energy Efficient In-Network Processing. Workshop on Bio-Inspired Information Pathways [Abstracts and Posters]. https://research.rug.nl/en/publications/memristor-based-cognitive-and-energy-efficient-in-network-process
  13. Sultana, N., Hossain, S. M. Z., Almuhaini, S., & Düştegör, D. (2022). Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand. Energies 2022, Vol. 15, Page 3425, 15(9), 3425. https://doi.org/10.3390/EN15093425
  14. Boughzala, B., Koldehofe, B., & Gärtner, C. (2022, May 26). Window-based Parallel Operator Execution with In-Network Computing: Proceedings. Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS ’22). https://doi.org/10.1145/3524860.3539804
  15. Kundel, R., Siegmund, F., Hark, R., Rizk, A., & Koldehofe, B. (2022). Network Testing Utilizing ProgrammableNetworking Hardware. IEEE Communications Magazine, 7 pages. https://research.rug.nl/en/publications/network-testing-utilizing-programmable-networking-hardware
  16. Gärtner, C., Rizk, A., Koldehofe, B., Guillaume, R., Kundel, R., & Steinmetz, R. (2022). On the Incremental Reconfiguration of Time-sensitive Networks at Runtime. Proceedings of the IFIP Networking Conference., 9 pages.
  17. Razavi, K., Luthra, M., Koldehofe, B., Mühlhäuser, M., & Wang, L. (2022). FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees. Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), 14 pages.
  18. Volnes, E., Plagemann, T., Koldehofe, B., & Goebel, V. (2022). Travel light: state shedding for efficient operator migration. Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS’22), 79–84. https://doi.org/10.1145/3524860.3539638
  19. Agnihotri, P., Koldehofe, B., Binnig, C., & Luthra, M. (2022). PANDA: performance prediction for parallel and dynamic stream processing. Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, 180–181. https://doi.org/10.1145/3524860.3543281
  20. Gärtner, C., Rizk, A., Koldehofe, B., Guillaume, R., Kundel, R., & Steinmetz, R. (2022). Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations. Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, 2 pages. https://doi.org/10.15496/PUBLIKATION-67440