PAINE Demo: Optimizing Video Selection Queries with Commonsense Knowledge

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611581

Wenjia He, Ibrahim Sabek, Yuze Lou, Michael Cafarella

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引用次数: 0

Abstract

Because video is becoming more popular and constitutes a major part of data collection, we have the need to process video selection queries --- selecting videos that contain target objects. However, a naïve scan of a video corpus without optimization would be extremely inefficient due to applying complex detectors to irrelevant videos. This demo presents Paine; a video query system that employs a novel index mechanism to optimize video selection queries via commonsense knowledge. Paine samples video frames to build an inexpensive lossy index, then leverages probabilistic models based on existing commonsense knowledge sources to capture the semantic-level correlation among video frames, thereby allowing Paine to predict the content of unindexed video. These models can predict which videos are likely to satisfy selection predicates so as to avoid Paine from processing irrelevant videos. We will demonstrate a system prototype of Paine for accelerating the processing of video selection queries, allowing VLDB'23 participants to use the Paine interface to run queries. Users can compare Paine with the baseline, the SCAN method.

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PAINE演示:用常识优化视频选择查询

由于视频越来越受欢迎，并且构成了数据收集的主要部分，我们需要处理视频选择查询——选择包含目标对象的视频。然而，由于将复杂的检测器应用于不相关的视频，因此在没有优化的情况下对视频语料库进行naïve扫描将非常低效。这个演示展示了Paine;视频查询系统采用一种新的索引机制，通过常识知识优化视频选择查询。Paine对视频帧进行采样以建立一个廉价的有损索引，然后利用基于现有常识知识来源的概率模型来捕获视频帧之间的语义级相关性，从而允许Paine预测未索引视频的内容。这些模型可以预测哪些视频可能满足选择谓词，从而避免Paine处理不相关的视频。我们将演示Paine的系统原型，用于加速视频选择查询的处理，允许VLDB'23参与者使用Paine接口来运行查询。用户可以与Paine基线进行比较，采用SCAN方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.