Impact and influence of modern AI in metadata management
- URL: http://arxiv.org/abs/2501.16605v1
- Date: Tue, 28 Jan 2025 00:44:38 GMT
- Title: Impact and influence of modern AI in metadata management
- Authors: Wenli Yang, Rui Fu, Muhammad Bilal Amin, Byeong Kang,
- Abstract summary: Metadata plays a critical role in data governance, resource discovery, and decision-making in the data-driven era.
This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives.
A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets.
The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges.
- Score: 11.734679079886131
- License:
- Abstract: Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.
Related papers
- Towards Human-Guided, Data-Centric LLM Co-Pilots [53.35493881390917]
CliMB-DC is a human-guided, data-centric framework for machine learning co-pilots.
It combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing.
We show how CliMB-DC can transform uncurated datasets into ML-ready formats.
arXiv Detail & Related papers (2025-01-17T17:51:22Z) - Deep Learning, Machine Learning, Advancing Big Data Analytics and Management [26.911181864764117]
Advances in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management.
This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies.
It equips researchers, practitioners, and data enthusiasts with the tools to navigate the complexities of modern data analytics.
arXiv Detail & Related papers (2024-12-03T05:59:34Z) - A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments [1.2753215270475886]
This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments.
We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual approach leveraging advanced machine learning techniques.
Key components include an intelligent data ingestion layer, adaptive preprocessing mechanisms, context-aware feature extraction, and AI-based quality assessment modules.
arXiv Detail & Related papers (2024-10-11T07:06:36Z) - Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications [17.624263707781655]
Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management.
This article delves into the foundational concepts and cutting-edge developments in these fields.
By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management.
arXiv Detail & Related papers (2024-10-02T06:24:51Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Integrative Approaches in Cybersecurity and AI [0.0]
We identify key trends, challenges, and future directions that hold the potential to revolutionize the way organizations protect, analyze, and leverage their data.
Our findings highlight the necessity of cross-disciplinary strategies that incorporate AI-driven automation, real-time threat detection, and advanced data analytics to build more resilient and adaptive security architectures.
arXiv Detail & Related papers (2024-08-12T01:37:06Z) - The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
Large Language Models (LLMs) are foundational to AI advancements.
LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information.
Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
arXiv Detail & Related papers (2024-03-23T09:26:15Z) - Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals
and Industrial Pragmatism [8.938344250520851]
We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility.
This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications.
We propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes.
arXiv Detail & Related papers (2024-03-04T08:29:15Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets.
We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers.
Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
arXiv Detail & Related papers (2023-11-22T22:15:17Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.