
International Journal on Science and Technology
E-ISSN: 2229-7677
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Impact Factor: 9.88
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 16 Issue 2
April-June 2025
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Revolutionizing Data Preparation And Access For Visual And Multi-Modal Business Analytics
Author(s) | Oyeronke Ladapo |
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Country | Nigeria |
Abstract | In the evolving landscape of business analytics, data preparation remains a critical yet time-consuming task, particularly when dealing with unstructured data such as images, videos, and associated metadata. Traditional systems often rely on ad-hoc solutions that are inefficient for handling large-scale visual data. This paper introduces the Visual Data Management System (VDMS), a novel framework designed to optimize the management, preparation, and access of visual data, while enabling seamless integration with machine learning and analytics pipelines. VDMS is built to handle massive datasets, such as images and videos, alongside feature vectors and metadata, making it an ideal tool for business applications in retail, healthcare, and media analytics. By centralizing data management, VDMS eliminates the need for complex, fragmented systems and simplifies data access through a unified API, significantly reducing data preparation time and improving the efficiency of analytics workflows. Our evaluation, using a 13TB dataset from the YFCC100M collection, demonstrates that VDMS outperforms traditional systems by up to 35 times, especially in large-scale environments. This paper highlights VDMS’s ability to enhance business analytics, enabling faster, more accurate insights from diverse data sources, and paving the way for more efficient, data-driven decision-making in modern enterprises. |
Keywords | Data Preparation, Machine Learning Integration, Analytics Pipeline, Unified API, Business Analytics |
Field | Computer > Data / Information |
Published In | Volume 16, Issue 2, April-June 2025 |
Published On | 2025-05-17 |
DOI | https://doi.org/10.71097/IJSAT.v16.i2.3490 |
Short DOI | https://doi.org/g9kf6q |
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IJSAT DOI prefix is
10.71097/IJSAT
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