International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Beyond the Horizon: A Framework for Next-Generation Data Science and the Evolution of Predictive Models |
|---|---|
| ABSTRACT | The discipline of data science stands at a critical juncture. While current paradigms, centered on statistical learning and deep neural networks, have yielded transformative insights, they increasingly struggle with the complexities of modern data ecosystems: extreme dimensionality, temporal non-stationarity, intricate causality, and the imperative for explainability and ethical compliance. This paper posits that "Next-Generation Data Science" (NGDS) will be defined not by incremental improvements in existing algorithms, but by a fundamental architectural and philosophical shift towards integrated, causal, and autonomous learning systems. We present a comprehensive framework that synthesizes three core pillars: (1) Causal-Representational Learning, which unifies deep representation learning with structural causal models to move beyond correlation to intervention and counterfactual reasoning; (2) Federated & Synthetic Data Ecosystems, enabling privacy-preserving, collaborative model development across siloed data sources via federated learning and high-fidelity synthetic data generation; and (3) Autonomous Meta-Learning Systems, where models are equipped with the ability to self-diagnose, self-correct, and actively design their own learning processes and data acquisition strategies. A novel methodological approach, combining large-scale simulation of digital twins and a longitudinal case study in precision oncology, was employed. We developed a simulation environment to test a Causal Graph Neural Network (Causal-GNN) against standard GNNs and MLPs on synthetic and semi-synthetic datasets with known ground-truth causal structures. Results demonstrated that the Causal-GNN achieved superior out-of-distribution (OOD) generalization, with a 40% lower error rate under significant distribution shift, and provided actionable counterfactual explanations. In the oncology case study, a federated meta-learning system deployed across three hospitals improved 12-month survival prediction accuracy by 22% compared to single-institution models, while preserving patient privacy. The analysis reveals that the key differentiator of NGDS will be predictive robustness—the ability to maintain accuracy in novel, evolving environments—rather than mere accuracy on i.i.d. test sets. We conclude that the future of prediction lies in systems that understand why phenomena occur, can learn collaboratively without sharing raw data, and can autonomously adapt to the unknown. This necessitates a reimagining of data science education, tooling, and governance to prioritize causality, privacy-by-design, and systemic resilience. |
| AUTHOR | M. PREETHA, C. SUMATHI Assistant Professor, Dept. of Artificial Intelligence and Data Science, Jerusalem College of Engineering, Pallikaranai, Chennai, India Assistant Professor, Dept. of Artificial Intelligence and Data Science, Anand Institute of Higher Technology, Kazhipattur, Chennai, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312093 |
| pdf/93_Beyond the Horizon A Framework for Next-Generation Data Science and the Evolution of Predictive Models.pdf | |
| KEYWORDS | |
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