2025· International Journal of Data Engineering and Intelligent Computing· 0 citations
TL;DR
An Autonomous Data Fabric architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems is proposed.
Abstract
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.
As enterprises transition toward increasingly distributed and cloud-native architectures, the need for seamless data interoperability has become paramount. Traditional data integration and governance approaches often fall short in dynamic, multi-cloud environments. Autonomous Data Products (ADPs) emerge as a transformative paradigm—self-contained, self-describing, and AI-enabled units that encapsulate data, metadata, policies, and processing logic. This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems. We discuss how AI enables adaptive schema evolution, smart data discovery, and automated quality checks within ADPs, and how they align with principles of data mesh and data fabric. Through technical frameworks and real-world use cases, we demonstrate how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.
Arjun Malhotra· International Journal of Dat...· 0 citations
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
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional centralized analytics face challenges related to privacy, security, scalability, and regulatory compliance. To address these issues, this study proposes an Adaptive Federated Analytics Framework (AFAF) for distributed enterprise data systems. The framework enables collaborative analytics without sharing raw data by incorporating adaptive node selection, dynamic aggregation, privacy-preserving mechanisms, and communication optimization techniques. Unlike conventional federated approaches, AFAF dynamically evaluates node reliability, computational capacity, data quality, and network conditions to improve analytical performance. The framework integrates differential privacy, secure multi-party computation (SMPC), and encrypted aggregation to ensure enterprise-grade security and compliance. Experimental evaluations in hybrid cloud enterprise environments demonstrate improvements in analytical accuracy, aggregation efficiency, communication overhead, convergence stability, scalability, and fault tolerance compared with traditional federated systems. The framework also supports real-time analytics through adaptive participation thresholds and aggregation frequencies, enabling timely insights from distributed data sources. Results indicate that AFAF provides a scalable, secure, and efficient platform for privacy-preserving enterprise intelligence, with future enhancements including AI-driven orchestration, blockchain-based trust management, and autonomous analytics optimization.
K. R., B. S. Shah· International Journal of App...· 0 citations
Centralized data operations are often using in Small and medium-sized enterprises (SMEs) for easy data management, but there suffering from few limitations like cloud platform integration with bigdata yet many still face difficulties integrating cloud platforms with big-data capabilities in a scalable and governed manner. To address the problems, this communication presents an Adaptive Cloud -Big-Data Enablement Framework (ACBDEF), which is a realistic mechanism of SME digital data transformation. This framework consists of five main steps, which are technological infrastructure, data governance and compliance, organizational capability development, environmental alignment, and intelligence/value extraction into a common architecture. One of the key elements in this work is the Adaptive Migration Engine (AME) which is used to assess dynamically workloads, the parameters such as data characteristics, regulatory constraints, the cost-performance metrics are used to decide on the optimal deployment in cloud premises, or in hybrid environments. The adaptive decision process is beneficial in assisting SMEs in mitigating technical and organizational issues in enhancing the efficiency, security, and analytical responsiveness. The proposed mechanism brings into line theoretical adoption factors with actionable implementation which provides a structured model for supporting SMEs to achieve sustainable and data-driven cloud transformation.
B. Madhu Uthej, Dudde Lohith, Atluru Sai Charan Reddy et al.· 2026 7th International Confe...· 0 citations
The rapid evolution of Property Technology (PropTech) has transformed enterprise real estate software from isolated operational applications into intelligent digital ecosystems that integrate cloud computing, artificial intelligence, enterprise software, and large-scale data platforms. Traditional property management systems were primarily designed to record transactions, automate administrative workflows, and support operational reporting. Contemporary organizations, however, increasingly require platforms capable of orchestrating intelligent decision-making across leasing, facility management, asset performance, sustainability, financial operations, customer engagement, and strategic portfolio management. This article argues that the future of PropTech should be understood through the perspective of enterprise architecture rather than software development alone. It proposes a strategic architectural framework that integrates business capabilities, cloud-native infrastructure, AI platform services, enterprise applications, governance mechanisms, and digital operations into a unified enterprise ecosystem. Unlike conventional software-centric approaches, the proposed framework emphasizes interoperability, architectural scalability, operational resilience, explainable artificial intelligence, and continuous organizational evolution. The study further demonstrates how artificial intelligence should function as an enterprise capability embedded throughout digital operations instead of existing as an isolated technological feature. Recent developments in enterprise architecture similarly emphasize that future competitive advantage will depend less on individual AI models and more on organizations' ability to integrate intelligence, cloud infrastructure, governance, and enterprise workflows into coherent operational ecosystems.
D. Tuncay· International Journal of Res...· 0 citations
The rapid growth of enterprise systems and cloud computing has transformed data management across hybrid environments integrating on-premise databases, private clouds, and public cloud infrastructures. However, challenges such as data consistency, latency, conflict resolution, security, and fault tolerance remain critical in distributed heterogeneous systems. Traditional synchronization methods are often inadequate for dynamic real-time workloads. This study reviews intelligent data synchronization techniques for hybrid data platforms, emphasizing AI- and machine learning-based approaches that enhance synchronization efficiency, scalability, and reliability. The proposed framework includes four layers: Data Acquisition, Intelligent Synchronization Engine, Adaptive Conflict Management, and Distributed Analytics. Predictive learning algorithms optimize synchronization timing and resource allocation, while adaptive conflict resolution mechanisms minimize inconsistencies. Experimental results show that intelligent synchronization methods reduce delay, improve throughput, enhance scalability, and strengthen failure recovery compared to traditional approaches. The study concludes that AI-driven synchronization is essential for real-time analytics, distributed transactions, and scalable cloud-native applications in modern enterprise environments.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
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