Copertina

Algorithmic Business Process Optimization: Empowering Operational Excellence with Service-Oriented Architecture (SOA) and Microservices

Salvato in:
Pubblicato in:Ingenierie des Systemes d'Information vol. 29, no. 6 (Dec 2024), p. 2399
Autore principale: Fatima Zohra Trabelsi
Altri autori: Khtira, Amal, Bouchra El Asri
Pubblicazione:
International Information and Engineering Technology Association (IIETA)
Soggetti:
Software
Customer satisfaction
Recommender systems
Computer architecture
Competitive advantage
Adaptability
Trends
Business process management
Continuous improvement
Data analysis
Modularity
Automation
Machine learning
Service oriented architecture
Customization
Internet of Things
Case studies
Innovations
Data mining
Cluster analysis
Artificial intelligence
Edge computing
Modular systems
Prediction models
User satisfaction
Clustering
Decision making
Cost reduction
Empowerment
Optimization
Flexibility
Industrial applications
Algorithms
Supply chains
Blockchain
Industrial development
Real time
Cloud computing
Financial services
Customer services
Vector quantization
Product development
Accesso online:Citation/Abstract
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract: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.
ISSN:1633-1311
2116-7125
1290-2926
DOI:10.18280/isi.290627
Fonte:Engineering Database