Big Data: Past, Present, and Future Insights

Abdalla, Hemn Barzan; Awlla, Ardalan Hussein; Kumar, Yulia; Cheraghy, Maryam

doi:10.1145/3685767.3685777

Proceedings of the 2024 Asia Pacific Conference on Computing Technologies, Communications and Networking 2024

DOI: 10.1145/3685767.3685777

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Big Data: Past, Present, and Future Insights

Hemn Barzan Abdalla,

Ardalan Hussein Awlla,

Yulia Kumar

et al.

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2024

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Cited by 2 publications

References 15 publications

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The AI-Powered Evolution of Big Data

Kumar,

Marchena,

Awlla

et al. 2024

Applied Sciences

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The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.

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The AI-Powered Evolution of Big Data

Kumar,

Marchena,

Awlla

et al. 2024

Applied Sciences

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A Comprehensive Review of AI Advancement Using testFAILS and testFAILS-2 for the Pursuit of AGI

Kumar,

Lin,

Paredes

et al. 2024

Electronics

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In a previous paper we defined testFAILS, a set of benchmarks for measuring the efficacy of Large Language Models in various domains. This paper defines a second-generation framework, testFAILS-2 to measure how current AI engines are progressing towards Artificial General Intelligence (AGI). The testFAILS-2 framework offers enhanced evaluation metrics that address the latest developments in Artificial Intelligence Linguistic Systems (AILS). A key feature of this re-view is the “Chat with Alan” project, a Retrieval-Augmented Generation (RAG)-based AI bot inspired by Alan Turing, designed to distinguish between human and AI generated interactions, thereby emulating Turing’s original vision. We assess a variety of models, including ChatGPT-4o-mini and other Small Language Models (SLMs), as well as prominent Large Language Models (LLMs), utilizing expanded criteria that encompass result relevance, accessibility, cost, multimodality, agent creation capabilities, emotional AI attributes, AI search capacity, and LLM-robot integration. The analysis reveals that testFAILS-2 significantly enhances the evaluation of model robustness and user productivity, while also identifying critical areas for improvement in multimodal processing and emotional reasoning. By integrating rigorous evaluation standards and novel testing methodologies, testFAILS-2 advances the assessment of AILS, providing essential insights that contribute to the ongoing development of more effective and resilient AI systems towards achieving AGI.

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Big Data: Past, Present, and Future Insights

Cited by 2 publications

References 15 publications

The AI-Powered Evolution of Big Data

The AI-Powered Evolution of Big Data

A Comprehensive Review of AI Advancement Using testFAILS and testFAILS-2 for the Pursuit of AGI

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