2018· International Journal of Data Engineering and Intelligent Computing· Vol 1, pp. 01-13· 0 citations
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.
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
Yuvaraj Kavala· International Journal of Com...· 0 citations
Enterprise metadata is essential for data discovery, governance, integration, and analytics. Traditional metadata engineering relies on manual, rule-based approaches that struggle with dynamic, heterogeneous enterprise data across cloud, IoT, ERP, CRM, and data lake environments. This study proposes a Large Language Model-Assisted Metadata Engineering Framework (LLM-MEF) that automates metadata extraction, semantic enrichment, schema recommendation, lineage discovery, and governance validation using transformer-based LLMs, Retrieval-Augmented Generation (RAG), vector databases, and knowledge graphs. The framework improves metadata quality, semantic consistency, discoverability, governance compliance, and operational efficiency while reducing manual effort. Explainable AI and continuous feedback learning further enhance transparency and adaptive improvement. The proposed LLM-MEF provides a scalable and intelligent solution for enterprise metadata management, supporting modern data governance, AI, business intelligence, regulatory compliance, and digital transformation.
David Wheeler, Michael Gordon· International Journal of Dat...· 0 citations
The exponential growth of organizational data assets has rendered traditional manual data governance approaches inadequate for modern Data Management Offices (DMOs). Existing frameworks lack the scalability, adaptability, and intelligence required to address the complexities of heterogeneous, high-velocity data environments. This paper proposes the AI-Augmented Intelligent Data Management Office (AI-iDMO) framework, a comprehensive six-layer architecture that integrates cutting-edge artificial intelligence including natural language processing (NLP), deep learning, federated learning, knowledge graphs, and reinforcement learning to automate and optimize core data governance functions. The AI-iDMO framework encompasses automated metadata management, real-time data quality monitoring, policy compliance enforcement, intelligent data lineage tracking, and adaptive access control, all orchestrated through a unified AI governance engine. We formally define each framework component using mathematical notations and present a reference implementation evaluated on benchmark enterprise datasets. Experimental results demonstrate that AI-iDMO achieves a classification accuracy of 96.8%, an F1-score of 0.967, a precision of 0.971, and a recall of 0.963, outperforming five state-of-the-art baselines. The framework offers a scalable, explainable, and privacy-preserving approach to intelligent data governance, providing practical guidance for DMOs navigating increasingly stringent regulatory requirements such as GDPR, CCPA, and HIPAA.
A. Alharbi, Abdullah Al, Malaise Al Ghamdi et al.· Journal of Intelligent Decis...· 0 citations
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
The increasing presence of heterogeneous data sources in modern information systems has intensified the need for intelligent data integration processes capable of handling semantic complexity, structural diversity, and dynamic changes. Traditional data integration methods, primarily based on relational schemas and syntactic mappings, struggle to address semantic heterogeneity in large-scale distributed environments. Knowledge graphs have emerged as a powerful paradigm, enabling semantically rich, flexible, and scalable integration by representing data as interconnected entities with metadata, ontologies, and inference capabilities. Using technologies such as RDF and OWL, knowledge graphs support interoperability, contextual reasoning, and unified data views across systems. This paper examines knowledge graph-based intelligent data integration systems, focusing on their architecture, methodology, and practical applications. It highlights their advantages in schema alignment, entity resolution, and semantic enrichment over traditional ETL approaches. The integration of machine learning techniques further enhances automation in data mapping, anomaly detection, and knowledge discovery. A systematic framework is proposed, covering ontology design, data ingestion, graph construction, and query optimization. A conceptual case study demonstrates improved integration accuracy, scalability, and query performance. Evaluation results indicate enhanced data quality, interoperability, and reasoning capabilities, along with reduced integration latency. Overall, knowledge graphs serve as a key enabler for next-generation intelligent data integration, supporting complex relationships and data-driven decision-making. Future work includes improving scalability, real-time processing, and integration with deep learning models.
Muhammad Al-Azar· International Journal of App...· 0 citations
This paper explores automated data transformation using intelligent rule-based systems to address the limitations of traditional ETL processes, which are often manual, rigid, and error-prone. The proposed approach integrates rule-based reasoning with metadata-driven transformation to handle complex data from heterogeneous sources such as structured, semi-structured, and unstructured data. The system features a modular architecture including data ingestion, rule definition, execution, and validation. It applies condition–action rules for tasks like normalization, filtering, aggregation, and enrichment, along with a feedback mechanism for continuous improvement. Experimental results show that the approach significantly reduces transformation time while maintaining accuracy and improving data quality. Challenges such as rule conflicts, scalability, and legacy integration are also addressed through strategies like rule prioritization and hybrid architectures. The study concludes that intelligent rule-based systems offer a scalable and efficient solution for modern data transformation in big data and real-time environments.
Tom DeMarco· International Journal of Dat...· 0 citations
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