Cognitive Data Engineering Frameworks for Automated Data Management
TL;DR
This paper presents a Cognitive Data Engineering Framework (CDEF) designed to automate key data lifecycle processes such as ingestion, transformation, integration, quality assurance, and governance through self-learning and context-aware capabilities.
Abstract
Cognitive Data Engineering (CDE) is an advanced paradigm that integrates artificial intelligence, machine learning, and knowledge-based systems into traditional data engineering to enable automated and intelligent data management. This paper presents a Cognitive Data Engineering Framework (CDEF) designed to automate key data lifecycle processes such as ingestion, transformation, integration, quality assurance, and governance. Unlike conventional rule-based pipelines, the proposed framework adapts dynamically to data changes, anomalies, and schema evolution through self-learning and context-aware capabilities. The framework employs metadata-driven intelligence, semantic modeling, reinforcement learning, and cognitive agents within a layered architecture comprising perception, reasoning, learning, and execution. It also leverages knowledge graphs and ontologies to enhance semantic interoperability and data discovery. Experimental results demonstrate improved performance, reduced errors, and increased flexibility compared to traditional systems. Overall, the study highlights the potential of CDEFs in enabling efficient, scalable, and autonomous data management, with future scope in edge computing, real-time analytics, and self-governing data ecosystems.