Abstract
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.
Highlights
Academic Editor: AdriánEnergy efficiency is among the top priorities of the European Union, and for that reason, a European framework [1] as well as a number of directives [2,3] have been established.These initiatives generate a vast amount of diverse data, which are yet fragmented, and, as a result, they cannot be utilized for data analysis, intelligent querying, and extraction of patterns
The data are produced from the data pre-processing and semantic enrichment layer, where they data are produced from the data pre-processing and semantic enrichment layer, where are homogenized and pre-processed before being sent to the reasoning engine through they are homogenized and pre-processed before being sent to the reasoning engine through Kafka topics
The results from the Engine data represented in the graph database, and queries using the Reasoning Kafka
Summary
Energy efficiency is among the top priorities of the European Union, and for that reason, a European framework [1] as well as a number of directives [2,3] have been established. These initiatives generate a vast amount of diverse data, which are yet fragmented, and, as a result, they cannot be utilized for data analysis, intelligent querying, and extraction of patterns. The need of graph analytics arises, aiming for the extraction and visualization of these data, as well as the creation of knowledge databases that will contain information about energy performance analytics and the related buildings. The analysis and process of these data can be beneficial in the monitoring [7] and management of energy consumption [8,9,10,11], as well as in the decision-making process regarding energy consumption [12,13]
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