scholarly journals A Model-Based Framework for Developing and Deploying Data Aggregation Services

2006 ◽  
pp. 227-239 ◽  
Author(s):  
Ramakrishna Soma ◽  
Amol Bakshi ◽  
V. K. Prasanna ◽  
Will Da Sie
Keyword(s):  
Data Aggregation ◽  
Model Based

Trust and grey model based data aggregation algorithm

2021 ◽  
Vol 35 (4) ◽  
pp. 258
Author(s):  
Ni Wang ◽  
Li Li ◽  
Yansui Du ◽  
Jun Wang
Keyword(s):  
Data Aggregation ◽  
Grey Model ◽  
Model Based

Data aggregation in multi-source assessment model based on evidence theory

2018 ◽  
Vol 69 ◽  
pp. 443-452 ◽  
Author(s):  
Hossein Nahid Titkanloo ◽  
Abbas Keramati ◽  
Roxana Fekri
Keyword(s):  
Data Aggregation ◽  
Evidence Theory ◽  
Assessment Model ◽  
Model Based ◽  
Source Assessment

scholarly journals Model-based Approach to Multi-domain Monitoring Data Aggregation

2021 ◽  
Author(s):  
Antonio Pastor ◽  
Diego R. López ◽  
Jose Ordonez-Lucena ◽  
Sonia Fernández ◽  
Jesús Folgueira
Keyword(s):  
Data Aggregation ◽  
Service Management ◽  
Closed Loop ◽  
Ad Hoc ◽  
Data Sources ◽  
Management Mechanism ◽  
Model Based ◽  
Closed Loop Systems ◽  
Disparate Data ◽  
Network Orchestration

The essential propellant for any closed-loop management mechanism is data related to the managed entity. While this is a general evidence, it becomes even more true when dealing with advanced closed-loop systems like the ones supported by Artificial Intelligence (AI), as they require a trustworthy, up-to-date and steady flow of state data to be applicable. Modern network infrastructures provide a vast amount of disparate data sources, especially in the multi-domain scenarios considered by the ETSI Industry Specification Group (ISG) Zero Touch Network and Service Management (ZSM) framework, and proper mechanisms for data aggregation, pre-processing and normalization are required to make possible AI-enabled closed-loop management. So far, solutions proposed for these data aggregation tasks have been specific to concrete data sources and consumers, following ad-hoc approaches unsuitable to address the vast heterogeneity of data sources and potential data consumers. This paper presents a model-based approach to a data aggregator framework, relying on standardized data models and telemetry protocols, and integrated with an open-source network orchestration stack to support their incorporation within network service lifecycles.

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