Publications

Uélison Jean Lopes dos Santos, Alessandro Ferri, Szilard Nistor, Riccardo Tommasini, Carsten Binnig, Manisha Luthra (2025). Towards a Multimodal Stream Processing System. EDBT 2026.

Roman Heinrich, Oleksandr Havrylov, Manisha Luthra, Johannes Wehrstein, Carsten Binnig (2025). Opening The Black-Box: Explaining Learned Cost Models For Databases. Proc. VLDB Endow. 18(12), 2025.

Roman Heinrich, Xiao Li, Manisha Luthra, Zoi Kaoudi (2025). Learned Cost Models for Query Optimization: From Batch to Streaming Systems. Proc. VLDB Endow. 18(12), 2025.

Roman Heinrich, Manisha Luthra, Johannes Wehrstein, Harald Kornmayer, Carsten Binnig (2025). How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks. Proc. ACM Manag. Data (SIGMOD 2025).

Wang Yue, Martin Boissier, Manisha Luthra, Tilmann Rabl (2025). Dema: Efficient Decentralized Aggregation for Non-Decomposable Quantile Functions. EDBT 2025.

Pratyush Agnihotri, Boris Koldehofe, Roman Heinrich, Carsten Binnig, Manisha Luthra (2024). PDSP-Bench: A Benchmarking System for Parallel and Distributed Stream Processing. TPCTC 2024.

Pratyush Agnihotri, Boris Koldehofe, Paul Stiegele, Roman Heinrich, Carsten Binnig, Manisha Luthra (2024). ZERoTuNE: Learned Zero-Shot Cost Models for Parallelism Tuning in Stream Processing. IEEE ICDE 2024.

Roman Heinrich, Carsten Binnig, Harald Kornmayer, Manisha Luthra (2024). COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments. IEEE ICDE 2024.

Mikhail Fomichev, Manisha Luthra, Maik Benndorf, Pratyush Agnihotri (2023). No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems. ACM DEBS 2023.

Lasse Thostrup, Gloria Doci, Nils Boeschen, Manisha Luthra, Carsten Binnig (2023). Distributed GPU Joins on Fast RDMA-capable Networks. To appear in ACM SIGMOD 2023.

Pratyush Agnihotri, Boris Koldehofe, Carsten Binnig, Manisha Luthra (2023). Zero-Shot Cost Models for Parallel Stream Processing. aiDM@SIGMOD 2023.

Manisha Luthra, Andreas Kipf, Matthias Böhm (2023). A Tutorial Workshop on ML for Systems and Systems for ML. BTW 2023.

Roman Heinrich, Manisha Luthra, Harald Kornmayer, Carsten Binnig (2022). Zero-shot cost models for Distributed Stream Processing. ACM DEBS 2022.

Johannes Wehrstein, Benjamin Hilprecht, Benjamin Olt, Manisha Luthra, Carsten Binnig (2022). The Case for Multi-Task Zero-Shot Learning for Databases. AIDB workshop in VLDB 2022.

Pratyush Agnihotri, Boris Koldehofe, Carsten Binnig, Manisha Luthra (2022). PANDA: Performance Prediction for Parallel and Dynamic Stream Processing. ACM DEBS 2022.

Kamran Razavi, Manisha Luthra, Boris Koldehofe, Max Mühlhäuser, Lin Wang (2022). FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees. IEEE RTAS 2022.

Manisha Luthra, Boris Koldehofe, Niels Danger, Pascal Weisenberger, Guido Salvaneschi, Ioannis Stavrakakis (2021). TCEP: Transitions in operator placement to adapt to dynamic network environments. Journal of Computer and System Sciences.

Manisha Luthra, Sebastian Hennig, Kamran Razavi, Lin Wang, Boris Koldehofe (2020). Operator as a Service: Stateful Serverless Complex Event Processing. IEEE BigData 2020.

Manisha Luthra, Boris Koldehofe, Jonas Höchst, Patrick Lampe, Ali Haider Rizvi, Ralf Kundel, Bernd Freisleben (2019). INetCEP: In-Network Complex Event Processing for Information-Centric Networking. IEEE ANCS.