Leveraging Graph Analytics for Energy Efficiency Certificates

As energy efficiency is becoming a subject of utter importance in today's societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. After thorough analysis and exploration, these data could provide a variety of solutions and optimizations regarding the energy efficiency subject. However, all the potential solutions that could derive from the aforementioned procedures still remain untapped due to the fact that these data are yet fragmented and highly sophisticated. In this paper, we propose an architecture for a Reasoning Engine, a mechanism that provides intelligent querying, insights and search capabilities, by leveraging technologies that will be described below. The proposed architecture has been developed in the context of the H2020 project called MATRYCS. In this paper, the reasons that resulted from the need of efficient ways of querying and analyzing the large amounts of data are firstly explained. Subsequently, several use cases, where related technologies were used to address real-world challenges, are presented. The main focus, however, is put in the detailed presentation of our Reasoning Engine's implementation steps. Lastly, the outcome of our work is demonstrated, showcasing the derived results and the optimizations that have been implemented.

Automated summary

This paper introduces an architecture for a Reasoning Engine to address the increasing need for efficient ways of querying and analyzing large amounts of data on energy efficiency. Developed within the context of the H2020 project MATRYCS, the Reasoning Engine provides intelligent querying, insights, and search capabilities. The paper explains the reasons for efficient data querying and analysis, presents use cases of related technologies addressing real-world challenges, and focuses on the detailed implementation steps of the Reasoning Engine.