AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines

• URL: http://arxiv.org/abs/2510.23408v1
• Date: Mon, 27 Oct 2025 15:11:31 GMT
• Title: AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines
• Authors: Abolfazl Younesi, Zahra Najafabadi Samani, Thomas Fahringer,
• Abstract summary: AutoStreamPipe is a framework that employs Large Language Models (LLMs) to automate the design, generation, and deployment of stream processing pipelines.<n>We show that AutoStreamPipe significantly reduces development time (x6.3) and error rates (x5.19), as measured by a novel Error-Free Score (EFS)
• Score: 0.764671395172401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data pipelines are essential in stream processing as they enable the efficient collection, processing, and delivery of real-time data, supporting rapid data analysis. In this paper, we present AutoStreamPipe, a novel framework that employs Large Language Models (LLMs) to automate the design, generation, and deployment of stream processing pipelines. AutoStreamPipe bridges the semantic gap between high-level user intent and platform-specific implementations across distributed stream processing systems for structured multi-agent reasoning by integrating a Hypergraph of Thoughts (HGoT) as an extended version of GoT. AutoStreamPipe combines resilient execution strategies, advanced query analysis, and HGoT to deliver pipelines with good accuracy. Experimental evaluations on diverse pipelines demonstrate that AutoStreamPipe significantly reduces development time (x6.3) and error rates (x5.19), as measured by a novel Error-Free Score (EFS), compared to LLM code-generation methods.

Related papers

• LLM-AutoDiff: Auto-Differentiate Any LLM Workflow [58.56731133392544]
  We introduce LLM-AutoDiff: a novel framework for Automatic Prompt Engineering (APE)<n>LLMs-AutoDiff treats each textual input as a trainable parameter and uses a frozen backward engine to generate feedback-akin to textual gradients.<n>It consistently outperforms existing textual gradient baselines in both accuracy and training cost.
  arXiv  Detail & Related papers  (2025-01-28T03:18:48Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline.<n>Recent works have started exploiting large language models (LLM) to lessen such burden.<n>This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster [61.83949316226113]
  FastCoT is a model-agnostic framework based on parallel decoding. We show that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach.
  arXiv  Detail & Related papers  (2023-11-14T15:56:18Z)
• Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications [7.850979932441607]
  Pathway is a new unified data processing framework that can run workloads on both bounded and unbounded data streams. We describe the system and present benchmarking results which demonstrate its capabilities in both batch and streaming contexts.
  arXiv  Detail & Related papers  (2023-07-12T08:27:37Z)
• Deep Pipeline Embeddings for AutoML [11.168121941015015]
  AutoML is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. Existing Pipeline Optimization techniques fail to explore deep interactions between pipeline stages/components. This paper proposes a novel neural architecture that captures the deep interaction between the components of a Machine Learning pipeline.
  arXiv  Detail & Related papers  (2023-05-23T12:40:38Z)
• eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems [12.933957727351666]
  Finding the right AI/ML model is a complex and costly process. We propose eTOP Framework which works on top of any AutoML system.
  arXiv  Detail & Related papers  (2023-04-17T20:22:30Z)
• SubStrat: A Subset-Based Strategy for Faster AutoML [5.833272638548153]
  SubStrat is an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small subset. It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset.
  arXiv  Detail & Related papers  (2022-06-07T07:44:06Z)
• Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines [77.45213180689952]
  Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. We introduce a new perspective on efficiently preparing datasets for end-to-end deep learning pipelines. We obtain an increased throughput of 3x to 13x compared to an untuned system.
  arXiv  Detail & Related papers  (2022-02-17T14:31:58Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation [13.116806430326513]
  We propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR) The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets' characteristics. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components.
  arXiv  Detail & Related papers  (2020-11-21T14:05:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.