Name
Abstract | ||
|---|---|---|
The discovery and exploitation of hidden information in collected data have gained attention in many areas, particularly in the energy field due to their economic and environmental impact. Data mining techniques have then emerged as a suitable toolbox for analyzing the data collected in modern network management systems in order to obtain a meaningful insight into consumption patterns and equipment operation. However, the enormous amount of data generated by sensors, occupational, and meteorological data involve the use of new management systems and data processing. Big Data presents great opportunities for implementing new solutions to manage these massive data sets. In addition, these data present values whose nature complicates and hides the understanding and interpretation of the data and results. Therefore, the use of fuzzy methods to adequately transform the data can improve their interpretability. This article presents an automatic fuzzification method implemented using the Big Data paradigm, which enables, in a later step, the detection of interrelations and patterns among different sensors and weather data recovered from an office building. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TFUZZ.2020.2992180 | IEEE Transactions on Fuzzy Systems |
Keywords | DocType | Volume |
Big Data,data mining,energy building,fuzzy association rules,operational research | Journal | 28 |
Issue | ISSN | Citations |
11 | 1063-6706 | 2 |
PageRank | References | Authors |
0.38 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Fernandez-Basso | 1 | 7 | 2.50 |
| M. Dolores Ruiz | 2 | 114 | 12.49 |
| Maria J. Martín-Bautista | 3 | 208 | 23.79 |