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IJET Best Journal: Revolutionizing Video Data Management with the HyperFractal Database Framework

IJET Best Journal: Revolutionizing Video Data Management with the HyperFractal Database Framework

Submit your research to IJET – the best low cost engineering journal for innovative video data management research.

IJET Best Journal Video Data Management

IJET Best Journal Authors & Affiliations

  • Dr Ravirajan K, Associate Principal, LTIMindtree, USA (ravirajan.k@ltimindtree.com)
  • Arvind Sundarajan, Senior Director, LTIMindtree, Poland
  • Sana Zia Hassan, Senior Manager, Ernst & Young, USA

Published: October, 2024

Abstract – IJET Best Journal

The AI-driven world of video streaming analytics has started to go even farther than anyone could imagine. State-of-the-art video solutions using deep learning techniques, as well as real-time alerts, are what is driving the revolution in the digital era. The HyperFractal Database is a new content management system which is designed particularly for video and is fully efficient. This system has a multi-tiered architecture, allowing video data to be handled effectively and efficiently through the use of advanced techniques. The key components are powerful video preprocessing and feature extraction, which transform the video into a format ready for the analysis. It in addition improves the arrangement of the video segments so that the storage amount is reduced and the retrieval speed goes up. This provides the user with the ability to edit video with the help of natural language queries resulting in an intuitive process. The database also boosts the video compression and uses smart indexing, so that users can quickly access large amounts of data. It is also capable of executing metadata queries in an efficient manner and speeding up the processing of spatial queries, while at the same time, it can effectively manage time-sensitive data. The practical results obtained show the actual improvements in storage and retrieval efficiency, which activate its usage in different fields, such as media production as well as surveillance. This research is a platform for future large-scale tasks of video data management, and it underlines its capacity to drastically change the way we deal with intricate video data and how we may access them.

Keywords – IJET Best Journal

Video Retrieval · Data Preprocessing · Metadata Management · Multimedia Management · Advanced Algorithms · International Journal of Engineering and Techniques (IJET) · Best Journal · Low Cost Journal

Full Text – Revolutionizing Video Data Management in IJET Best Journal

The AI-driven world of video streaming analytics has started to go even farther than anyone could imagine. State-of-the-art video solutions using deep learning techniques, as well as real-time alerts, are what is driving the revolution in the digital era. The HyperFractal Database is a new content management system which is designed particularly for video and is fully efficient. This system has a multi-tiered architecture, allowing video data to be handled effectively and efficiently through the use of advanced techniques. The key components are powerful video preprocessing and feature extraction, which transform the video into a format ready for the analysis. It in addition improves the arrangement of the video segments so that the storage amount is reduced and the retrieval speed goes up. This provides the user with the ability to edit video with the help of natural language queries resulting in an intuitive process. The database also boosts the video compression and uses smart indexing, so that users can quickly access large amounts of data. It is also capable of executing metadata queries in an efficient manner and speeding up the processing of spatial queries, while at the same time, it can effectively manage time-sensitive data. The practical results obtained show the actual improvements in storage and retrieval efficiency, which activate its usage in different fields, such as media production as well as surveillance. This research is a platform for future large-scale tasks of video data management, and it underlines its capacity to drastically change the way we deal with intricate video data and how we may access them.

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