Publications

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Conference papers
Journal papers
Workshop papers
Others
Theses

Conference Papers

  1. Jawad Tahir, Ruben Mayer, Christoph Doblander, and Hans-Arno Jacobsen. “How Reliable Are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems.” In PVLDB, 18(3): 585-598. https://doi.org/10.14778/3712221.3712227

  2. Nikolai Merkel, Pierre Toussing, Ruben Mayer, and Hans-Arno Jacobsen. “Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study. ”In PVLDB, 18(2): 293-307. https://doi.org/10.14778/3705829.3705846

  3. Herbert Woisetschläger, Alexander Erben, Ruben Mayer, Shiqiang Wang, and Hans-Arno Jacobsen. “FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems.” In Proceedings of the 25th ACM/IFIP International Middleware Conference 2024 (Middleware ’24). Pages 88 – 102. https://doi.org/10.1145/3652892.3700751

  4. Nikolai Merkel, Daniel Stoll, Ruben Mayer, and Hans-Arno Jacobsen. “An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training.” In Proceedings of the 28th International Conference on Extending Database Technology (EDBT ’25). 14 Pages. https://dx.doi.org/10.48786/edbt.2025.14

  5. Herbert Woisetschläger, Alexander Erben, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen. “A Survey on Efficient Federated Learning Methods for Foundation Model Training.” In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI ’24). Pages 8317–8325. https://doi.org/10.24963/ijcai.2024/919

  6. Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. “Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization.” In 2024 International Joint Conference on Neural Networks (IJCNN). 8 Pages. https://doi.org/10.1109/IJCNN60899.2024.10650820

  7. Alexander Erben, Ruben Mayer, and Hans-Arno Jacobsen. “How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study.” In PVLDB, 17(6): 1214–1226, 2024. https://doi.org/10.14778/3648160.3648165

  8. Manuel Weber, Farzan Banihashemi, Peter Mandl, Hans-Arno Jacobsen, and Ruben Mayer. “Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection.” In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ‘23). Pages 1–10. https://doi.org/10.1145/3600100.3623718

  9. Frank Christian Geyer, Hans-Arno Jacobsen, Ruben Mayer, and Peter Mandl. “An End-to-End Performance Comparison of Seven Permissioned Blockchain Systems.” In Proceedings of the 24th ACM/IFIP International Middleware Conference (Middleware ‘23). Pages 71–84. https://doi.org/10.1145/3590140.3629106

  10. Pezhman Nasirifard, Ruben Mayer, and Hans-Arno Jacobsen. “OrderlessChain: A CRDT-based Coordination-free Blockchain Without Global Order of Transactions.” In Proceedings of the 24th ACM/IFIP International Middleware Conference (Middleware ‘23). Pages 137–150. https://doi.org/10.1145/3590140.3629111

  11. René Schwermer, Ekin-Alp Bicer, Pascal Schirmer, Ruben Mayer, and Hans-Arno Jacobsen. “Federated Computing in Electric Vehicles to Predict Coolant Temperature (Industry Track).” In Proceedings of the 24th ACM/IFIP International Middleware Conference: Industrial Track (Middleware ‘23). Pages 8–14. https://doi.org/10.1145/3626562.3626829

  12. René Schwermer, Ruben Mayer, and Hans-Arno Jacobsen. “Energy vs Privacy: Estimating the Ecological Impact of Federated Learning.” In Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy ‘23). Pages 347–352. https://doi.org/10.1145/3575813.3597344

  13. Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschläger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong. “A Survey on Dataset Distillation: Approaches, Applications and Future Directions.” In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI ’23). Pages 6610–6618. https://doi.org/10.24963/ijcai.2023/741

  14. Nikolai Merkel, Ruben Mayer, Tawkir Ahmed Fakir, and Hans-Arno Jacobsen. “Partitioner Selection with EASE to Optimize Distributed Graph Processing.” In Proceedings of the 2023 IEEE 39th International Conference on Data Engineering (ICDE ’23). Pages 2400–2414. https://doi.org/10.1109/ICDE55515.2023.00185

  15. Jeeta Ann Chacko, Ruben Mayer, and Hans-Arno Jacobsen. “How To Optimize My Blockchain? A Multi-Level Recommendation Approach.” In Proceedings of the ACM on Management of Data (PACMMOD) 1, 1, Article 24 (May 2023), 27 pages. https://doi.org/10.1145/3588704
    New publication format for ACM SIGMOD conference papers since 2023!

  16. René Schwermer, Jonas Buchberger, Ruben Mayer, and Hans-Arno Jacobsen. “Federated Office Plug-Load Identification for Building Management Systems.” In Proceedings of the 13th ACM International Conference on Future Energy Systems (e-Energy ‘22). Pages 114-126. https://doi.org/10.1145/3538637.3538845

  17. Ruben Mayer, Kamil Orujzade, and Hans-Arno Jacobsen. “Out-of-Core Edge Partitioning at Linear Run-Time.” In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE ‘22). Pages 2630-2643. https://doi.org/10.1109/ICDE53745.2022.00242

  18. Alexander Isenko, Ruben Mayer, Jeffrey Jedele, and Hans-Arno Jacobsen. “Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines.” In Proceedings of the 2022 ACM SIGMOD International Conference on Management of Data (SIGMOD ‘22). Pages 1825-1839. https://doi.org/10.1145/3514221.3517848
    Result reproduced!

  19. Ruben Mayer and Hans-Arno Jacobsen. 2021. “Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints.” In Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data (SIGMOD ‘21). Pages 1289–1302. https://doi.org/10.1145/3448016.3457300

  20. Jeeta Ann Jacko, Ruben Mayer, and Hans-Arno Jacobsen. 2021. “Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric.” In Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data (SIGMOD ‘21). Pages 221–234. https://doi.org/10.1145/3448016.3452823

  21. Pezhman Nasirifard, Ruben Mayer, and Hans-Arno Jacobsen. 2019. “FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains.” In Proceedings of the 20th ACM/IFIP International Middleware Conference (Middleware ‘19). Pages 110–122. https://doi.org/10.1145/3361525.3361540

  22. Christian Mayer, Ruben Mayer, Sukanya Bhowmik, Lukas Epple, and Kurt Rothermel. 2018. “HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion.” In Proceedings of the 2018 IEEE International Conference on Big Data (BigData ’18). Pages 458-467. https://doi.org/10.1109/BigData.2018.8621968

  23. Christian Mayer, Ruben Mayer, Muhammad Adnan Tariq, Heiko Geppert, Larissa Laich, Lukas Rieger, and Kurt Rothermel. 2018. “ADWISE: Adaptive Window-Based Streaming Edge Partitioning for High-Speed Graph Processing.” In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS ‘18). Pages 685-695. https://doi.org/10.1109/ICDCS.2018.00072

  24. Ruben Mayer, Ahmad Slo, Muhammad Adnan Tariq, Kurt Rothermel, Manuel Gräber, and Umakishore Ramachandran. 2017. “SPECTRE: Supporting Consumption Policies in Window-Based Parallel Complex Event Processing.” In Proceedings of the 18th ACM/IFIP/USENIX International Middleware Conference (Middleware ‘17). Pages 161-173. https://doi.org/10.1145/3135974.3135983

  25. Thomas Bach, Muhammad Adnan Tariq, Ruben Mayer, and Kurt Rothermel. 2017. “Knowledge is at the Edge! How to Search in Distributed Machine Learning Models.” In Proceedings of OTM / CoopIS 2017 (OTM ‘17). Pages 410-428. https://doi.org/10.1007/978-3-319-69462-7_27

  26. Ruben Mayer, Leon Graser, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran. 2017. “EmuFog: Extensible and Scalable Emulation of Large-Scale Fog Computing Infrastructures.” In Proceedings of the 1st IEEE Fog World Congress (FWC ‘17). 6 pages. https://doi.org/10.1109/FWC.2017.8368525

  27. Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran. 2017. “FogStore: Toward a Distributed Data Store for Fog Computing.” In Proceedings of the 1st IEEE Fog World Congress (FWC ‘17). 6 pages. https://doi.org/10.1109/FWC.2017.8368524

  28. Ruben Mayer, Muhammad Adnan Tariq, and Kurt Rothermel. 2017. “Minimizing Communication Overhead in Window-Based Parallel Complex Event Processing.” In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS ‘17). Pages 54-65. https://doi.org/10.1145/3093742.3093914

  29. Ruben Mayer, Boris Koldehofe, and Kurt Rothermel. 2014. “Meeting Predictable Buffer Limits in the Parallel Execution of Event Processing Operators.” In Proceedings of the 2014 IEEE International Conference on Big Data (BigData ‘14). Pages 402-411. https://doi.org/10.1109/BigData.2014.7004257

  30. Boris Koldehofe, Ruben Mayer, Umakishore Ramachandran, Kurt Rothermel, and Marco Völz. 2013. “Rollback-recovery without checkpoints in distributed event processing systems.” In Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (DEBS ‘13). Pages 27-38. https://doi.org/10.1145/2488222.2488259

Journal Papers

  1. Jana Vatter, Ruben Mayer, and Hans-Arno Jacobsen. 2023. “The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey.” ACM Computing Surveys 56, 1, Article 6 (January 2024). 37 pages. https://doi.org/10.1145/3597428

  2. Ruben Mayer and Hans-Arno Jacobsen. 2020. “Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools.” ACM Computing Surveys 53, 1, Article 3 (May 2020), 37 pages. https://doi.org/10.1145/3363554

  3. Henriette Röger and Ruben Mayer. 2019. “A Comprehensive Survey on Parallelization and Elasticity in Stream Processing.” ACM Computing Surveys 52, 2, Article 36 (May 2019), 37 pages. https://doi.org/10.1145/3303849

  4. Christian Mayer, Muhammad Adnan Tariq, Ruben Mayer, and Kurt Rothermel. 2018. “GrapH: Traffic-Aware Graph Processing.” In IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 6, June 2018. Pages 1289-1302. https://doi.org/10.1109/TPDS.2018.2794989

  5. Ruben Mayer, Boris Koldehofe, and Kurt Rothermel. 2015. “Predictable Low-Latency Event Detection with Parallel Complex Event Processing.” In IEEE Internet of Things Journal, vol. 2, no. 4, Aug. 2015. Pages 274-286. https://doi.org/10.1109/JIOT.2015.2397316

Workshop Papers

  1. Barbara Hoffman and Ruben Mayer. “Comparing Methods for Bias Mitigation in Graph Neural Networks.” Workshop on Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data), in conjunction with AAAI 2025.

  2. Jana Vatter, Maurice L Rochau, Ruben Mayer, Hans-Arno Jacobsen. “To What Extent Does Quality Matter? The Impact of Graph Data Quality on GNN Model Performance.” In Proceedings of the 4th International Workshop on Large-Scale Graph Data Analytics (LSGDA ’25), in conjunction with VLDB 2025. 10 Pages.

  3. Jana Vatter, Maurice L Rochau, Ruben Mayer, Hans-Arno Jacobsen. “Size Does (Not) Matter? Sparsification and Graph Neural Network Sampling for Large-scale Graphs.” In Proceedings of the 3rd International Workshop on Large-Scale Graph Data Analytics (LSGDA ’24) in conjunction with VLDB 2024. 10 Pages.

  4. Barbara Hoffmann, Jana Vatter, and Ruben Mayer. “Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act.” In Proceedings of the 3rd International Workshop on Large-Scale Graph Data Analytics (LSGDA ’24) in conjunction with VLDB 2024. 10 Pages.

  5. Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, and Michael Katz. 2024. “Choosing a Classical Planner with Graph Neural Networks.” In Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP ’24) in conjunction with ICAPS 2024. 9 Pages.

  6. Herbert Woisetschläger, Alexander Erben, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen. 2024. “Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly”. In Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning (DEEM ’24) in conjunction with SIGMOD 2024. Pages 39–50. https://doi.org/10.1145/3650203.3663331

  7. Jeeta Ann Chacko, Ruben Mayer, Alan Fekete, Vincent Gramoli, and Hans-Arno Jacobsen. 2023. “A Comprehensive Study on Benchmarking Permissioned Blockchains.” In Proceedings of Technology Conference on Performance Evaluation and Benchmarking (TPCTC ’23) in conjunction with VLDB 2023. 16 Pages. https://doi.org/10.1007/978-3-031-68031-1_2

  8. Jawad Tahir, Raj Mandal, Olha Stefanova, Hans-Arno Jacobsen, Christoph Doblander, and Ruben Mayer. 2023. “PTA: A Programmable Teaching Assistant for Lab Courses.” In Proceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research (DataEd ’23) in conjunction with SIGMOD 2023. Pages 24–29. https://doi.org/10.1145/3596673.3596975

  9. Thomas Kohler, Ruben Mayer, Frank Dürr, Marius Maaß, Sukanya Bhowmik, and Kurt Rothermel. 2018. “P4CEP: Towards In-Network Complex Event Processing.” In Proceedings of the 2018 Morning Workshop on In-Network Computing (NetCompute ‘18) in conjunction with SIGCOMM 2018. ACM. Pages 33–38. https://doi.org/10.1145/3229591.3229593

  10. Christian Mayer, Ruben Mayer, Jonas Grunert, Kurt Rothermel, and Muhammad Adnan Tariq. 2018. “Q-graph: Preserving Query Locality in Multi-Query Graph Processing.” In Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES-NDA ‘18) in conjunction with SIGMOD 2018. Article 6. 10 Pages. https://doi.org/10.1145/3210259.3210265

  11. Ruben Mayer, Christian Mayer, and Larissa Laich. 2017. “The TensorFlow Partitioning and Scheduling Problem: It’s the Critical Path!” In Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning (DIDL ‘17) in conjunction with MIDDLEWARE 2017. ACM. Pages 1-6. https://doi.org/10.1145/3154842.3154843

  12. Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran. 2017. “The Fog Makes Sense: Enabling Social Sensing Services with Limited Internet Connectivity.” In Proceedings of the 2nd International Workshop on Social Sensing, (SocialSens ‘17), ACM. Pages 61-66. https://doi.org/10.1145/3055601.3055614

Others (Demos, Posters, etc.)

  1. Barbara Hoffmann, Marcos Martinez Galindo, Ruben Mayer, Vanessa Lopez Garcia, and Thanh Lam Hoang. “Multimodal Structured Data to LLM Adaptation System.” In IEEE International Conference on Data Mining (ICDM), Demo Track.

  2. Luca De Martini, Jawad Tahir, Alessandro Margara, Christoph Doblander, Sebastian Frischbier, Ruben Mayer, and Hans-Arno Jacobsen. “The DEBS 2025 Grand Challenge: Real-Time Monitoring of Defects in Laser Powder Bed Fusion (L-PBF) Manufacturing.” In Proceedings of the 19th ACM International Conference on Distributed and Event-based Systems (DEBS ’25). Pages 223–228. https://doi.org/10.1145/3701717.3735578

  3. Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer and Hans-Arno Jacobsen. “Detecting Trading Trends in Financial Tick Data: the DEBS 2022 Grand Challenge.” In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS ’22). Pages 132–138.

  4. Murtaza Raza, Jawad Tahir, Christoph Doblander, Ruben Mayer, and Hans-Arno Jacobsen. “Benchmarking Apache Kafka under Network Faults.” In Proceedings of the 22nd International Middleware Conference: Demos and Posters (Middleware ’21). Pages 5–7.

  5. Rinita Roy, Ruben Mayer, and Hans-Arno Jacobsen. “MDMS: Music Data Matching System for Query Variant Retrieval”. In Proceedings of the 29th ACM International Conference on Multimedia (MM ‘21). Pages 2762–2764.

  6. Jawad Tahir, Christoph Doblander, Ruben Mayer, Sebastian Frischbier, and Hans-Arno Jacobsen. “The DEBS 2021 Grand Challenge: Analyzing Environmental Impact of Worldwide Lockdowns”. In Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems (DEBS ’21). Pages 136–141.

  7. Vincenzo Gulisano, Daniel Jorde, Ruben Mayer, Hannaneh Najdataei, and Dimitris Palyvos-Giannas. “The DEBS 2020 Grand Challenge”. In Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems (DEBS ‘20). Pages 183–186.

  8. Christian Mayer, Ruben Mayer, and Majd Abdo. “StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge”. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS ‘17). Pages 298-303.

  9. Enrique Saurez, Harshit Gupta, Umakishore Ramachandran, and Ruben Mayer. “Fog Computing for Improving User Application Interaction and Context Awareness: Demo Abstract”. In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation (IoTDI ‘17). Pages 281-282.

  10. Ruben Mayer, Christian Mayer, Muhammad Adnan Tariq, and Kurt Rothermel. “GraphCEP – Real-time Data Analytics Using Parallel Complex Event and Graph Processing”. In Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS ‘16). Pages 309-316.

  11. Beate Ottenwälder, Ruben Mayer, and Boris Koldehofe. “Distributed Complex Event Processing for Mobile Large-Scale Video Applications”. In Proceedings of the Posters & Demos Session (Middleware Posters and Demos ‘14). Pages 5-6.

  12. Ruben Mayer. “Real-Time Distributed Complex Event Processing for Big Data Scenarios”. PhD Workshop at 7th ACM International Conference on Distributed Event-Based Systems (DEBS ‘13). 3 Pages.

Theses

  1. Ruben Mayer. 2018. “Window-based Data Parallelization in Complex Event Processing”. Dissertation, University of Stuttgart.

  2. Ruben Mayer. 2012. “Recovery of Event Streams in Complex Event Processing Systems”. Diploma Thesis, University of Stuttgart.