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Title: EVA: An End-to-End Exploratory Video Analytics System
In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analyt- ics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has de- veloped video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical appli- cation. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage RAY’s distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap.  more » « less
Award ID(s):
1908984
PAR ID:
10538584
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; « less
Publisher / Repository:
ACM
Date Published:
ISBN:
9798400702044
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Location:
Seattle WA USA
Sponsoring Org:
National Science Foundation
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