Large language models (LLMs) are increasingly integrated into data management systems, yet their energy consumption remains largely unexplored. EcoLLM is a workload-centric benchmark for studying the energy-aware trade-offs of LLMs in data-processing …
In this paper, we present a vision for a new generation of multimodal streaming systems that embed MLLMs as first-class operators, enabling real-time query processing across multiple modalities. Achieving this is non-trivial: while recent work has …
Learned cost models for parallel stream processing typically rely on hand-engineered features, which requires substantial domain expertise and manual effort. This paper presents an automated feature selection pipeline that identifies the features …
Learned cost models (LCMs) have recently gained traction as a promising alternative to traditional cost estimation techniques in data management, offering improved accuracy by capturing complex interactions between queries, data, and runtime …
This paper presents the very first approach for opening the black box by bringing AI explainability approaches to Learned Cost Models (LCMs). New explanation techniques are proposed that extend and adapt existing methods for the general …
Query optimizers traditionally rely on cost models to choose the best execution plan, and while machine learning-based cost models have been proposed to overcome weaknesses of traditional models, limited efforts have been made to investigate how well …
The second edition of this tutorial workshop, co-located with BTW 2025 in Bamberg, Germany, explores the intersection of machine learning and systems, covering both the application of ML techniques to optimize and improve systems (ML for Systems) as …
COSTREAM provides a learned cost model for Distributed Stream Processing Systems that can accurately predict the execution costs of a streaming query in an edge-cloud environment. The model can be used to find an initial placement of operators across …
ZERoTuNE introduces a novel cost model for parallel and distributed stream processing that can be used to effectively set initial parallelism degrees of streaming queries. Unlike existing models, which rely majorly on online learning statistics that …
This paper presents zero-shot cost models for parallel stream processing, enabling accurate cost predictions for parallel streaming queries without having observed any query deployment. The approach leverages data-efficient zero-shot learning …