EVA互动演示

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:10.14778/3611540.3611626

Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj

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引用次数: 1

摘要

在这个演示中，我们将介绍EVA，一个端到端的ai关系数据库管理系统。我们将使用三种使用场景来演示EVA的功能和效用:(1)EVA作为使用Streamlit和React开发的探索性视频分析界面的后端，(2)EVA与Python和数据科学生态系统无缝集成，允许用户在Python笔记本中访问EVA以及其他流行的库，如Pandas和Matplotlib，以及(3)EVA便于使用Label Studio进行大量标记，这是一个广泛使用的标记框架。通过优化复杂的视觉查询，我们说明了EVA如何允许广泛的应用程序开发人员利用计算机视觉的最新进展。

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Interactive Demonstration of EVA

In this demonstration, we will present EVA, an end-to-end AI-Relational database management system. We will demonstrate the capabilities and utility of EVA using three usage scenarios: (1) EVA serves as a backend for an exploratory video analytics interface developed using Streamlit and React, (2) EVA seamlessly integrates with the Python and Data Science ecosystems by allowing users to access EVA in a Python notebook alongside other popular libraries such as Pandas and Matplotlib, and (3) EVA facilitates bulk labeling with Label Studio, a widely-used labeling framework. By optimizing complex vision queries, we illustrate how EVA allows a wide range of application developers to harness the recent advances in computer vision.

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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.