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Related Experiment Video

Updated: Dec 17, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

TORNADO: Intermediate Results Orchestration based Service-Oriented Data Curation Framework for Intelligent Video Big

Aftab Alam¹, Young-Koo Lee¹

¹Data and Knowledge Engineering Laboratory, Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, 17104, Yongin-si, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)

|July 1, 2020

Summary

The TORNADO framework offers a scalable cloud solution for intelligent video big data analytics. It enhances distributed video processing, providing researchers with flexible tools for algorithm deployment and improved system performance.

Keywords:

big data analytics distributed video analytics intermediate results orchestration service-oriented architecture video big data curation

Related Experiment Videos

Last Updated: Dec 17, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Computer Science
Cloud Computing
Big Data Analytics

Background:

Increasing surveillance cameras generate massive video data, necessitating distributed video analytics systems.
Existing solutions often rely on traditional client/server models, limiting scalability and flexibility.

Purpose of the Study:

To develop a scalable and flexible framework for intelligent video big data analytics in the cloud.
To provide high-level abstractions for deploying video analytics algorithms as a service.

Main Methods:

Developed the TORNADO framework using general-purpose big data technologies.
Utilized distributed stream and file management systems for acquiring video streams.
Proposed a unified IR Middleware for orchestrating intermediate analytics results.

Main Results:

Demonstrated the framework's performance, scalability, and fault tolerance.
Showcased the viability of the TORNADO framework for cloud-based video big data analytics.

Conclusions:

The TORNADO framework offers a robust solution for distributed video analytics in the cloud.
The proposed system improves scalability, fault tolerance, and overall performance for video big data processing.