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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Cloud Data Modeling Techniques for Modern Data Warehousing |
| Authors Name: | Prateek Panigrahy |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204835
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| Published Paper Id: | IJRTI2506148 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i6.204835 |
| Abstract: | Cloud process orchestration is now a cornerstone of operational efficiency in today's digital ecosystems. Nevertheless, traditional workflow engines are gradually becoming inadequate in managing the dynamic and high-dimensional nature of cloud-native applications and distributed systems. In this regard, this paper presents an exhaustive theoretical investigation of AI-facilitated optimization strategies in cloud process orchestration and workflow engines. It begins with a historical overview of workflow automation, followed by a taxonomy of AI methodologies such as predictive analytics, prescriptive optimization, reinforcement learning, semantic reasoning, and explainable AI. According to this, we present our novel model—the Cognitive Adaptive Orchestration Framework (CAOF) that integrates these AI techniques into an explainable, scalable, and modular orchestration system. The practicability of these techniques in the real world is demonstrated by case studies of leading organizations such as Netflix, Google, Siemens, and IBM. We consider the key obstacles to adoption: data problems, legacy integration, and the computational expense of advanced AI techniques. Finally, the paper outlines avenues for future research that include federated learning, light models, and ontology automation. The study offers a framework that can be utilized in creating next-generation orchestration platforms that are adaptive, transparent, and resilient key characteristics in the era of autonomous cloud computing. |
| Keywords: | Cloud Process Orchestration, AI-Driven Workflow Optimization, Workflow Engines |
| Cite Article: | "Cloud Data Modeling Techniques for Modern Data Warehousing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b386-b390, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506148.pdf |
| Downloads: | 000463 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2506148
Registration ID:204835
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i6.204835
Page No: b386-b390 Country: Chennai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506148 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506148 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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