arXiv subjects

Xinjie Hu

Publications and source records attributed to Xinjie Hu.

3 recordsLinked to original sources

arXiv2026

Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting

Materialized views (MVs) accelerate OLAP and data-warehouse workloads by precomputing reusable subexpressions, but practical MV-based query acceleration is a multi-stage pipeline: candidate enumeration, view selection under storage budgets, and query rewriting inside the optimizer. Existing evaluations typically study only parts of this pipeline and within a single system, leaving end-to-end trade-offs and cross-system behavior unclear. In this paper, we benchmark MV-based query rewriting by jointly evaluating enumeration, selection, and rewriting with a modular evaluation framework and by using controlled ablations. We also introduce a cross-engine protocol allowing us to compare systems that expose only execution plans by contrasting native optimizer-level rewriting with portable SQL rewriting baselines when available. Across representative academic methods and modern open-source and commercial systems, we find strong interaction effects across stages and large variability in MV usage and realized savings. We identify recurring failure modes that explain performance regressions after rewriting. Our results highlight which pipeline stages most often limit performance and provide evidence to guide future MV enumeration, selection, and rewriting designs.

cs.DB↗

arXiv2025

StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, the StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.

astro-ph.IM↗

arXiv2025

LightCurve MoE: A Dynamic Sparse Routing Mixture-of-Experts Architecture for Efficient Stellar Light Curve Classification

The classification of stellar light curves has become a key task in modern time-domain astronomy, fueled by the rapid growth of data from large-scale surveys such as Kepler and TESS. Although deep learning models have achieved high accuracy in this area, their computational costs can limit scalability. To tackle this issue, we propose LightCurve MoE, a Mixture-of-Experts (MoE) architecture that combines dynamic sparse routing with a dual-gating mechanism to balance accuracy, efficiency, and robustness. Our model includes five specialized experts, each using a different feature extraction method-such as wavelet transforms, Gramian angular fields, and recurrence plots-to capture unique patterns in the light curves. A dual-gating mechanism evaluates these expert outputs by analyzing both frequency and time-domain features, allowing the model to adaptively weigh each expert's contribution. During inference, only the top 3 out of 5 experts are activated per sample using a Top-k routing strategy, reducing computational cost by 40% compared to dense models while preserving strong accuracy (96%). The model also includes entropy regularization and a technique to retain inactive experts during training, ensuring stable and effective learning. By combining sparse computation with multi-modal feature fusion, LightCurve MoE offers a scalable solution for future large-scale photometric surveys like LSST and Global Open Transient Telescope Array (GOTTA), where processing efficiency is crucial due to the massive volume of daily data.

astro-ph.IM↗