Daily Archives: October 1, 2026

Authors: K. Sathish Kumar, Assistant Professor A. Ram Kumar

Abstract: This research investigates the development and structural performance of fly ash-based Geopolymer Concrete (GPC) reinforced with polypropylene fibers as a sustainable alternative to conventional Ordinary Portland Cement (OPC) concrete. The study addresses the growing environmental concerns associated with cement production, particularly the high emission of carbon dioxide and the disposal of industrial waste materials. By utilizing Class F fly ash as the primary alumina-silicate and activating it with sodium hydroxide and sodium silicate solutions, a cement-free geopolymer binder was produced to promote environmentally friendly construction practices. The experimental program focused on evaluating the influence of polypropylene fibers on the mechanical and micro structural properties of geopolymer concrete. Geopolymer mixtures were prepared using locally available fly ash, fine and coarse aggregates, and alkaline activators with different molar concentrations. Heat curing was adopted to accelerate the geopolymerization process and to achieve rapid strength development. The study examined important engineering properties including compressive strength, split tensile strength, flexural strength, modulus of elasticity, ultrasonic pulse velocity, and durability characteristics to assess the suitability of GPC for structural applications. In addition to mechanical evaluation, the research investigated the nano- and micro structural behaviour of polypropylene fibre-reinforced geopolymer concrete through characterization of pore volume, crystalline structure, morphology, surface area, and surface-active phases. These analyses provided insight into the formation of a dense alumina-silicate gel matrix and the role of fibers in controlling micro-crack propagation and improving the internal structure of the composite. The interaction between geopolymer binder and polypropylene fibers was found to enhance the integrity of the matrix while reducing brittleness. The results demonstrate that the incorporation of polypropylene fibers significantly improves tensile and flexural performance, crack resistance, and overall durability without compromising the environmental advantages of geopolymer concrete. Heat-cured fly ash geopolymer concrete exhibited excellent early-age strength, superior resistance to aggressive environments, and the potential to replace OPC concrete in sustainable infrastructure. The study concludes that polypropylene fibre-reinforced geopolymer concrete is a high-performance, eco-friendly construction material capable of contributing to low-carbon and durable structural development.

DOI: https://doi.org/10.5281/zenodo.23076276

Published by: vikaspatanker

Authors: Richard Taylor, Mark Allen, Joshua Baker, Chaitanya Srinivas, Niharika

Abstract: Modern enterprises increasingly depend on complex data pipelines to support real-time analytics, business intelligence, machine learning, and mission-critical decision-making. However, conventional data pipeline monitoring approaches are largely reactive, requiring manual intervention after failures, performance degradation, data quality issues, or infrastructure anomalies have already occurred. These limitations can result in increased downtime, data loss, processing delays, and operational costs. This research proposes a Proactive AI-Driven Framework for Intelligent Data Pipeline Monitoring and Self-Healing that integrates artificial intelligence, predictive analytics, anomaly detection, automated diagnostics, and intelligent remediation mechanisms to improve the reliability and resilience of enterprise data pipelines. The proposed framework continuously analyzes pipeline telemetry, including execution metrics, processing latency, failure patterns, resource utilization, logs, data-quality indicators, and dependency information. Machine learning models are employed to identify anomalous behavior and predict potential pipeline failures before they occur. Based on detected conditions, an intelligent decision engine determines appropriate corrective actions, such as task retry, resource optimization, dependency recovery, workload redistribution, configuration adjustment, or pipeline restart. A feedback mechanism continuously evaluates remediation outcomes and enhances future predictions and recovery decisions. The framework aims to reduce pipeline downtime, mean time to recovery (MTTR), false alerts, manual operational effort, and data-processing failures while improving pipeline availability, reliability, and operational efficiency. The proposed approach provides a scalable foundation for autonomous data operations and demonstrates how AI-driven predictive monitoring can transform traditional reactive pipeline management into a proactive and self-healing data engineering model.

DOI: http://doi.org/10.5281/zenodo.23076070

Published by: vikaspatanker

Authors: Paul Hall, George Cox, Edward Howard, Chaitanya Srinivas, Niharika

Abstract: The increasing complexity of enterprise data ecosystems has created significant challenges in maintaining accurate, transparent, and continuously updated data lineage across distributed databases, cloud platforms, data warehouses, data lakes, APIs, and analytical systems. Traditional data lineage approaches primarily depend on manually defined mappings, static metadata repositories, and rule-based tracking mechanisms, which can become difficult to maintain as enterprise data environments evolve rapidly. This research proposes an Intelligent Knowledge Graph Framework for Predictive Enterprise Data Lineage that integrates knowledge graph technologies, artificial intelligence, machine learning, metadata management, and automated lineage discovery to provide a scalable and intelligent approach to enterprise data traceability. The proposed framework represents data assets, business entities, transformations, dependencies, processes, and relationships as interconnected knowledge graph entities, enabling comprehensive representation and semantic analysis of enterprise data flows. Machine learning and predictive analytics mechanisms are incorporated to identify hidden dependencies, predict potential lineage changes, detect anomalous transformation patterns, and assess the impact of upstream modifications on downstream data assets. The framework further integrates automated metadata extraction, schema analysis, provenance tracking, and lineage inference to reduce manual lineage maintenance and improve data transparency. A continuous feedback mechanism enables the framework to update lineage relationships as new data sources, transformations, and business processes are introduced. The proposed approach can enhance enterprise data governance, impact analysis, regulatory compliance, data quality management, and decision-making by providing an intelligent and predictive view of data movement and dependencies. The effectiveness of the framework can be evaluated using metrics such as lineage discovery accuracy, relationship prediction accuracy, anomaly-detection precision, lineage completeness, metadata coverage, impact-analysis accuracy, and reduction in manual lineage management effort. The framework provides a foundation for developing adaptive, explainable, and intelligent data lineage capabilities for modern enterprise data platforms.

DOI: http://doi.org/10.5281/zenodo.23075967

Published by: vikaspatanker

Authors: Shad Ali Khan, Dr. Shikhar Kumar

Abstract: This research paper examines the concept of consumption Puzzle in the long-term context. An eye caching and an important problem in macroeconomics theory which economists have tried to solve for decades. The mirage of a given predicted relationship between income and consumption is yet to be supported by theories over the long term. The absolute theory of consumption is an important traditional theory. The theory says changing income will lead to change in consumption but only at a diminishing rate. This concept leads to formation of another important concept that is average propensity to consume (APC) will gradually decrease when the economy grows. According to this reasoning the long run economic growth will be achieved by raising the savings in the economy. Historical economic data shows a distinct trend when examining national income in the long term. As economics grew over many years, both income and spending increased together in almost the same. In simple words, when people earn more over the long term they also spend more and divide their income for future expenditures that make their consumption and income stable in the long run. But according to old theories if the income rises then people should save more of their income and the spending share will change. To solve the consumption puzzle many economists said people do not make spendings by only present income. In fact they think more broadly when their income is increased. The Permanent income hypothesis says that people spend their income on the basis of not only current income but also care about future income. Example: if someone's income increases for a short time or a one-time increment then they may save most of it because they know it is a temporary income change. But when the income changes permanently then they increase their consumption. The Life Cycle Hypothesis also provides a similar idea. It says people plan their spending over their whole life, they may save during the working year, make consumption and save for retirement and sustain their consumption even after retirement. However their low income doesn’t affect their consumption. People not only save for retirement but also have saving habits from generation for emergence funds, education and major household works.

DOI: https://doi.org/10.5281/zenodo.23075989

Published by: vikaspatanker

Authors: Steven Walker, Andrew Allen, Kenneth Young, Chaitanya Srinivas, Niharika

Abstract: The rapid adoption of artificial intelligence, cloud computing, big data analytics, and digital platforms has significantly increased the complexity of enterprise data management and governance. Organizations increasingly require mature data governance capabilities to ensure that enterprise data is accurate, secure, accessible, compliant, traceable, and suitable for intelligent decision-making. However, conventional data governance maturity models frequently depend on manually assessed capabilities, static evaluation criteria, and periodic assessments, limiting their ability to respond to rapidly changing data environments and emerging AI-driven requirements. This research proposes an AI-Enabled Data Governance Maturity Framework for Intelligent Enterprise Data Management that integrates artificial intelligence, machine learning, predictive analytics, data quality management, metadata management, data lineage, security, privacy, compliance, and governance automation into a unified maturity assessment approach. The proposed framework evaluates enterprise governance capabilities across multiple maturity dimensions, including data strategy, organizational governance, data quality, metadata management, lineage and traceability, security and privacy, regulatory compliance, technology enablement, AI governance, and automation. AI-driven analytical mechanisms are incorporated to assess governance performance, identify capability gaps, predict governance risks, and recommend prioritized improvement actions. The framework further introduces continuous monitoring and feedback mechanisms to enable organizations to dynamically update their maturity assessments as data environments, business requirements, technologies, and regulatory conditions evolve. A maturity scoring mechanism can classify organizations into progressive levels ranging from initial and developing governance practices to managed, intelligent, predictive, and optimized governance capabilities. The proposed approach aims to reduce manual assessment effort, improve governance transparency, strengthen data quality and compliance, and support continuous improvement of enterprise data management practices. The framework can be evaluated using metrics such as maturity assessment accuracy, governance-risk prediction accuracy, data-quality improvement, compliance effectiveness, automation rate, assessment efficiency, and reduction in governance-related incidents. The proposed framework provides a foundation for organizations seeking to transition from traditional governance practices toward intelligent, adaptive, and AI-enabled enterprise data governance.

DOI: http://doi.org/10.5281/zenodo.23075782

Published by: vikaspatanker

Authors: Frederick Mitchell, Patrick Edwards, Stephen Bailey, Chaitanya Srinivas, Niharika

Abstract: The rapid adoption of artificial intelligence (AI), cloud computing, and advanced analytics has increased the need for scalable, reliable, secure, and intelligent enterprise data architectures. Traditional data management environments often operate through fragmented systems, heterogeneous data sources, isolated processing platforms, and manually driven governance practices, limiting the ability of organizations to deliver timely and trusted data for AI-driven decision-making. This research proposes a Smart Data Architecture Framework for AI-Driven Enterprise Data Management that integrates enterprise data sources, cloud and distributed data platforms, intelligent data integration, metadata management, data governance, data quality, security, and AI-enabled analytics within a unified architectural model. The proposed framework emphasizes automated metadata discovery, intelligent data classification, real-time data quality monitoring, predictive anomaly detection, semantic data integration, lineage tracking, and policy-driven governance. AI and machine learning capabilities are incorporated to support adaptive data management, identify potential data-quality issues, optimize data-processing workflows, and improve the availability of trusted enterprise data for analytical and AI applications. The framework also incorporates security and privacy mechanisms to support controlled access, compliance, and responsible use of enterprise data. By establishing an integrated architecture across data ingestion, storage, processing, governance, intelligence, and consumption layers, the proposed approach aims to reduce data fragmentation, improve data reliability and accessibility, and strengthen organizational readiness for AI adoption. The framework provides a structured foundation for organizations seeking to modernize enterprise data management and develop scalable, intelligent, and governance-aware data ecosystems.

DOI: http://doi.org/10.5281/zenodo.23075696

Published by: vikaspatanker

Authors: Patrick Collins, Brian Campbell, Eric Wallace, Chaitanya Srinivas, Niharika

Abstract: The rapid growth of enterprise data has increased the complexity of documenting datasets, data structures, business definitions, data pipelines, metadata, and data lineage across heterogeneous information environments. Traditional data documentation practices frequently depend on manual processes, domain experts, and static documentation methods, resulting in incomplete, inconsistent, outdated, and difficult-to-maintain data descriptions. This research proposes an Automated Enterprise Data Documentation Using Generative Artificial Intelligence framework that leverages generative AI and natural language processing to automate the creation, enrichment, and maintenance of enterprise data documentation. The proposed framework integrates data discovery, schema analysis, metadata extraction, semantic interpretation, data lineage identification, business-term generation, and natural-language documentation into a unified architecture. Generative AI models are utilized to transform technical metadata and structural information into human-readable descriptions of datasets, tables, columns, relationships, transformation processes, and business rules. The framework also incorporates validation mechanisms to improve documentation accuracy, consistency, traceability, and governance. Automated documentation can be continuously updated when data structures, pipelines, or metadata change, thereby reducing documentation maintenance effort and improving data discoverability. The proposed approach supports data engineers, data stewards, analysts, governance teams, and business users by providing accessible and context-aware descriptions of enterprise data assets. The framework aims to establish a scalable and intelligent documentation process that improves metadata management, knowledge sharing, data governance, and organizational readiness for AI-driven data management.

DOI: http://doi.org/10.5281/zenodo.23075640

Published by: vikaspatanker