Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | |
| Veröffentlicht: |
Zenodo
2019
|
| Online-Zugang: | https://doi.org/10.5281/zenodo.15096221 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866902189311524864 |
|---|---|
| author | Raghavender Maddali |
| author_facet | Raghavender Maddali |
| contents | <p>Self-adaptive systems (SAS) are of interest in software engineering because they can automatically adapt their behavior to meet dynamic changes in the environment and changing requirements. SAS utilize adaptive architectures, machine learning, and control-based methods to provide end-to-end quality assurance while solving trade-offs among several quality attributes like performance, security, reliability, and scalability. Several methods such as feature-oriented adaptation, runtime learning, and stability-oriented methods have been suggested for enhancing the resilience and confidence of self-adaptive systems. The literature on self-adaptive systems offers methods such as continuous assurances, learning-driven validation frameworks, and real-time adaptive controllers for maximizing system performance as well as dependability. In addition to this, self-adaptive cloud autoscaling systems are also essential for dynamically regulating computation resources in cloud computing systems. Despite such progress, problems like handling uncertainty, verification at runtime, and governance of adaptation strategies continue. This article offers an introduction to major advances in the domain of SAS by focusing on the role of frameworks, taxonomies, and applied cases and the potential of various frameworks in areas of cloud computing, virtual reality, and service-oriented architectures. Some of the avenues for further research include increasing context-awareness, optimizing adaptive decision-making, and incorporating AI-based techniques toward smarter and active adaptation mechanisms.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15096221 |
| institution | Zenodo |
| language | |
| publishDate | 2019 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Self-Adaptive Data Quality Frameworks with Continuous Learning Mechanisms Raghavender Maddali <p>Self-adaptive systems (SAS) are of interest in software engineering because they can automatically adapt their behavior to meet dynamic changes in the environment and changing requirements. SAS utilize adaptive architectures, machine learning, and control-based methods to provide end-to-end quality assurance while solving trade-offs among several quality attributes like performance, security, reliability, and scalability. Several methods such as feature-oriented adaptation, runtime learning, and stability-oriented methods have been suggested for enhancing the resilience and confidence of self-adaptive systems. The literature on self-adaptive systems offers methods such as continuous assurances, learning-driven validation frameworks, and real-time adaptive controllers for maximizing system performance as well as dependability. In addition to this, self-adaptive cloud autoscaling systems are also essential for dynamically regulating computation resources in cloud computing systems. Despite such progress, problems like handling uncertainty, verification at runtime, and governance of adaptation strategies continue. This article offers an introduction to major advances in the domain of SAS by focusing on the role of frameworks, taxonomies, and applied cases and the potential of various frameworks in areas of cloud computing, virtual reality, and service-oriented architectures. Some of the avenues for further research include increasing context-awareness, optimizing adaptive decision-making, and incorporating AI-based techniques toward smarter and active adaptation mechanisms.</p> |
| title | Self-Adaptive Data Quality Frameworks with Continuous Learning Mechanisms |
| url | https://doi.org/10.5281/zenodo.15096221 |