Algorithmic Business Process Optimization: Empowering Operational Excellence with Service-Oriented Architecture (SOA) and Microservices
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| Publicat a: | Ingenierie des Systemes d'Information vol. 29, no. 6 (Dec 2024), p. 2399 |
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| Altres autors: | , |
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International Information and Engineering Technology Association (IIETA)
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.18280/isi.290627 |2 doi | |
| 035 | |a 3157167253 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 100 | 1 | |a Fatima Zohra Trabelsi | |
| 245 | 1 | |a Algorithmic Business Process Optimization: Empowering Operational Excellence with Service-Oriented Architecture (SOA) and Microservices | |
| 260 | |b International Information and Engineering Technology Association (IIETA) |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper introduces a novel approach to optimizing business processes by integrating principles from Service-Oriented Architecture (SOA), micro-services, and recommendation systems. Our approach leverages specific machine learning techniques such as clustering algorithms for behavioral segmentation and association rule mining for pattern identification, combined with data-driven insights derived from real-time process data. We propose a comprehensive algorithm that identifies inefficiencies in existing workflows, utilizing K-Means clustering and Apriori-based association rule mining to recommend optimized, modular architectures based on interoperable services. Additionally, the system provides personalized recommendations for ongoing improvements using predictive models. Through a detailed implementation, we demonstrate how our method enhances operational efficiency by reducing process redundancies, scalability through modular micro-services, and user satisfaction by streamlining service delivery. Preliminary results from case studies in the e-commerce and financial services sectors show up to 20% improvement in process execution time and 15% increase in customer satisfaction. Our approach differentiates itself from existing methods by offering a seamless integration of modular service architectures with real-time optimization and personalized feedback, creating a continuous improvement loop that adapts to changing business conditions. Finally, we discuss future research directions, including refining recommendation models, developing real-time optimization capabilities, and exploring applications in industry-specific contexts. | |
| 653 | |a Software | ||
| 653 | |a Customer satisfaction | ||
| 653 | |a Recommender systems | ||
| 653 | |a Computer architecture | ||
| 653 | |a Competitive advantage | ||
| 653 | |a Adaptability | ||
| 653 | |a Trends | ||
| 653 | |a Business process management | ||
| 653 | |a Continuous improvement | ||
| 653 | |a Data analysis | ||
| 653 | |a Modularity | ||
| 653 | |a Automation | ||
| 653 | |a Machine learning | ||
| 653 | |a Service oriented architecture | ||
| 653 | |a Customization | ||
| 653 | |a Internet of Things | ||
| 653 | |a Case studies | ||
| 653 | |a Innovations | ||
| 653 | |a Data mining | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Edge computing | ||
| 653 | |a Modular systems | ||
| 653 | |a Prediction models | ||
| 653 | |a User satisfaction | ||
| 653 | |a Clustering | ||
| 653 | |a Decision making | ||
| 653 | |a Cost reduction | ||
| 653 | |a Empowerment | ||
| 653 | |a Optimization | ||
| 653 | |a Flexibility | ||
| 653 | |a Industrial applications | ||
| 653 | |a Algorithms | ||
| 653 | |a Supply chains | ||
| 653 | |a Blockchain | ||
| 653 | |a Industrial development | ||
| 653 | |a Real time | ||
| 653 | |a Cloud computing | ||
| 653 | |a Financial services | ||
| 653 | |a Customer services | ||
| 653 | |a Vector quantization | ||
| 653 | |a Product development | ||
| 700 | 1 | |a Khtira, Amal | |
| 700 | 1 | |a Bouchra El Asri | |
| 773 | 0 | |t Ingenierie des Systemes d'Information |g vol. 29, no. 6 (Dec 2024), p. 2399 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3157167253/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3157167253/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |