An Ontology-Based Meta-modelling Approach for Semantic-Driven Building Management Systems
Lecture Notes in Business Information Processing, ISSN: 1865-1356, Vol: 521, Page: 200-211
2024
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures2
- Readers2
Conference Paper Description
The increase in smart buildings has led to an increase in data produced and consumed by buildings. Despite growing digitalisation trends, data interoperability, data quality, and a lack of transparency hinder the development of scalable energy applications. Knowledge graphs alleviate some of these challenges through their ability to integrate and analyse diverse data sources. Despite these benefits, knowledge graphs require specific skills typically uncommon in building and energy system engineers. This work tackles this challenge by enabling system engineers to create and maintain knowledge graphs about BMS by dealing with visual diagrammatical models they are familiar with. For this, we built on the ontology-based meta-modelling approach and created a proof-of-concept AOAME4BMS, in which we implemented a BMS and used it for evaluation purposes.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85196183851&origin=inward; http://dx.doi.org/10.1007/978-3-031-61003-5_18; https://link.springer.com/10.1007/978-3-031-61003-5_18; https://dx.doi.org/10.1007/978-3-031-61003-5_18; https://link.springer.com/chapter/10.1007/978-3-031-61003-5_18
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know