arXiv subjects

Dominik Winecki

Publications and source records attributed to Dominik Winecki.

4 recordsLinked to original sources

arXiv2026

Vega-Video: Integrating Video into the Grammar of Graphics

Video data is increasingly used alongside conventional data for interactive data exploration, necessitating interfaces for exploring and presenting mixed-modality data. However, integrating video into visualizations remains difficult due to its distinct paradigms and inherent performance challenges. We identify three classes of video data visualization - synchronization, annotation, and transformation - and integrate them into the Vega declarative grammar. We show that these abstractions enable high-performance implementation. To reconcile Vega's instantaneous dataflow with video player state, we introduce a split-signal architecture that preserves declarative semantics while masking video update delays. We detect continuous scrubbing interactions at compile time to apply encoding-aware optimizations that improve responsiveness by up to 4x. We also repurpose VOD protocols to transform videos in real time, delivering sub-200ms updates even on multi-hour-long compilations. These contributions enable seamless integration of conventional and video data visualization.

cs.HC↗

arXiv2026

Optimal Bounds-Only Pruning for Spatial AkNN Joins

We propose a bounds-only pruning test for exact Euclidean AkNN joins on partitioned spatial datasets. Data warehouses commonly partition large tables and store row group statistics for them to accelerate searches and joins, rather than maintaining indexes. AkNN joins can benefit from such statistics by constructing bounds and localizing join evaluations to a few partitions before loading them to build spatial indexes. Existing pruning methods are overly conservative for bounds-only spatial data because they do not fully capture its directional semantics, thereby missing opportunities to skip unneeded partitions at the earliest stages of a join. We propose a three-bound proximity test to determine whether all points within a partition have a closer neighbor in one partition than in another, potentially occluded partition. We show that our algorithm is both optimal and efficient.

cs.DB↗

arXiv2026

Vidformer: Drop-in Declarative Optimization for Rendering Video-Native Query Results

When interactively exploring video data, video-native querying involves consuming query results as videos, including steps such as compilation of extracted video clips or data overlays. These video-native queries are bottlenecked by rendering, not the execution of the underlying queries. This rendering is currently performed using post-processing scripts that are often slow. This step poses a critical point of friction in interactive video data workloads: even short clips contain thousands of high-definition frames; conventional OpenCV/Python scripts must decode -> transform -> encode the entire data stream before a single pixel appears, leaving users waiting for many seconds, minutes, or hours. To address these issues, we present Vidformer, a drop-in rendering accelerator for video-native querying which, (i) transparently lifts existing visualization code into a declarative representation, (ii) transparently optimizes and parallelizes rendering, and (iii) instantly serves videos through a Video on Demand protocol with just-in-time segment rendering. We demonstrate that Vidformer cuts full-render time by 2-3x across diverse annotation workloads, and, more critically, drops time-to-playback to 0.25-0.5s. This represents a 400x improvement that decouples clip length from first-frame playback latency, and unlocks the ability to perform interactive video-native querying with sub-second latencies. Furthermore, we show how our approach enables interactive video-native LLM-based conversational querying as well.

cs.DB↗

arXiv2024

Photometry of Saturated Stars with Neural Networks

We use a multilevel perceptron (MLP) neural network to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The MLP can obtain fairly unbiased photometry for stars from g~4 to 14~mag, particularly compared to the dispersion (15%-85% 1sigma range around the median) of 0.12 mag for saturated (g<11.5 mag) stars. More importantly, the light curve of a non-variable saturated star has a median dispersion of only 0.037 mag. The MLP light curves are, in many cases, spectacularly better than those provided by the standard ASAS-SN pipelines. While the network was trained on g band data from only one of ASAS-SN's 20 cameras, initial experiments suggest that it can be used for any camera and the older ASAS-SN V band data as well. The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars more than the MLP itself. The method is publicly available as a light curve option on ASAS-SN Sky Patrol v1.0.

astro-ph.SR↗