Microservices based Linked Data Quality Model for Buildings Energy Management Services

TL;DR

This paper proposes a microservices-based architecture with ontologies to improve and assess the quality of linked data used in buildings energy management, addressing data reliability issues from diverse sources.

Contribution

It introduces a novel microservices architecture combined with domain ontologies to enhance and evaluate energy-related linked data quality.

Findings

Improved data quality assessment methods for energy data.

Enhanced modularity and scalability in data processing.

Better reliability in energy management services.

Abstract

During the production, distribution, and consumption of energy, a large quantity of data is generated. For efficiently using of energy resources other supplementary data such as building information, weather, and environmental data etc. are also collected and used. All these energy data and relevant data is published as linked data in order to enhance the reusability of data and maximization of energy management services capability. However, the quality of this linked data is questionable because of wear and tears of sensors, unreliable communication channels, and highly diversification of data sources. The provision of high-quality energy management services requires high quality linked data, which reduces billing cost and improve the quality of the living environment. Assessment and improvement methodologies for the quality of data along with linked data needs to process very diverse data from highly diverse data sources. Microservices based data-driven architecture has great significance to processes highly diverse linked data with modularity, scalability, and reliability. This paper proposed microservices based architecture along with domain data and metadata ontologies to enhance and assess energy-related linked data quality.