Causify DataFlow: A Framework For High-performance Machine Learning Stream Computing

• URL: http://arxiv.org/abs/2512.23977v1
• Date: Tue, 30 Dec 2025 04:24:04 GMT
• Title: Causify DataFlow: A Framework For High-performance Machine Learning Stream Computing
• Authors: Giacinto Paolo Saggese, Paul Smith,
• Abstract summary: We present DataFlow, a computational framework for building, testing, and deploying machine learning systems on unbounded time-series data.<n>Traditional data science assume finite datasets and require substantial reimplementation when moving from batch prototypes to streaming production systems.<n>DataFlow resolves these issues through a unified execution model based on acyclic graphs with point-in-time idempotency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DataFlow, a computational framework for building, testing, and deploying high-performance machine learning systems on unbounded time-series data. Traditional data science workflows assume finite datasets and require substantial reimplementation when moving from batch prototypes to streaming production systems. This gap introduces causality violations, batch boundary artifacts, and poor reproducibility of real-time failures. DataFlow resolves these issues through a unified execution model based on directed acyclic graphs (DAGs) with point-in-time idempotency: outputs at any time t depend only on a fixed-length context window preceding t. This guarantee ensures that models developed in batch mode execute identically in streaming production without code changes. The framework enforces strict causality by automatically tracking knowledge time across all transformations, eliminating future-peeking bugs. DataFlow supports flexible tiling across temporal and feature dimensions, allowing the same model to operate at different frequencies and memory profiles via configuration alone. It integrates natively with the Python data science stack and provides fit/predict semantics for online learning, caching and incremental computation, and automatic parallelization through DAG-based scheduling. We demonstrate its effectiveness across domains including financial trading, IoT, fraud detection, and real-time analytics.

Related papers

• MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection [47.86433139298671]
  This paper introduces MAWIFlow, a flow-based benchmark derived from the MAWILAB v1.1 dataset.<n>The resulting datasets comprise temporally distinct samples from January 2011, 2016, and 2021, drawn from trans-Pacific backbone traffic.<n>Traditional machine learning methods, including Decision Trees, Random Forests, XGBoost, and Logistic Regression, are compared to a deep learning model based on a CNN-BiLSTM architecture.
  arXiv  Detail & Related papers  (2025-06-20T14:51:35Z)
• SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner [53.54568352375669]
  We introduce **SWE-Flow**, a novel data synthesis framework grounded in Test-Driven Development (TDD)<n>Unlike existing software engineering data that rely on human-submitted issues, **SWE-Flow** automatically infers incremental development steps directly from unit tests.<n>We generated 16,061 training instances and 2,020 test instances from real-world GitHub projects, creating the **SWE-Flow-Eval** benchmark.
  arXiv  Detail & Related papers  (2025-06-10T17:23:33Z)
• Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion [7.021277706390712]
  Missing intemporal systems presents a challenge for modern applications ranging from environmental monitoring to urban traffic management.<n>Current approaches based on machine learning and deep learning struggle to model thedependencies between spatial and temporal dimensions effectively.<n>CoFILL builds on the inherent advantages of diffusion-quality models to generate high-quality imputations.
  arXiv  Detail & Related papers  (2025-06-08T11:53:06Z)
• SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio [17.811771707446926]
  We show that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. We provide our trained model, SONNET, which is runnable in real-time and works on novel data out of the box for many real data applications.
  arXiv  Detail & Related papers  (2024-11-20T10:23:21Z)
• Streaming Factor Trajectory Learning for Temporal Tensor Decomposition [33.18423605559094]
  We propose Streaming Factor Trajectory Learning for temporal tensor decomposition. We use Gaussian processes (GPs) to model the trajectory of factors so as to flexibly estimate their temporal evolution. We have shown the advantage of SFTL in both synthetic tasks and real-world applications.
  arXiv  Detail & Related papers  (2023-10-25T21:58:52Z)
• 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)
• PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks [68.96484488899901]
  We present PARTIME, a library designed to speed up neural networks whenever data is continuously streamed over time. PARTIME starts processing each data sample at the time in which it becomes available from the stream. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning.
  arXiv  Detail & Related papers  (2022-10-17T14:49:14Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• 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)
• Tracking Performance of Online Stochastic Learners [57.14673504239551]
  Online algorithms are popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable accuracy. We establish a link between steady-state performance derived under stationarity assumptions and the tracking performance of online learners under random walk models.
  arXiv  Detail & Related papers  (2020-04-04T14:16:27Z)

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.