dc.contributor.author | Stander, Tiaan | |
dc.contributor.author | Rens, Johan | |
dc.date.accessioned | 2017-02-17T09:56:39Z | |
dc.date.available | 2017-02-17T09:56:39Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Stander, T. & Rens, J. 2014. Quality of supply data mining. IEEE 16th International Conference on Harmonics and Quality of Power (ICHQP), 25-28 May: 44-48. [https://doi.org/ 10.1109/ICHQP.2014.6842887] | en_US |
dc.identifier.isbn | 978-1-4673-6487-4 (Online) | |
dc.identifier.issn | 2164-0610 (Online) | |
dc.identifier.issn | 1540-6008 | |
dc.identifier.uri | http://hdl.handle.net/10394/20409 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/6842887 | |
dc.identifier.uri | https://doi.org/ 10.1109/ICHQP.2014.6842887 | |
dc.description.abstract | Extracting useful network management information from a large volume of QoS data obtained all over a network can be simplified by innovative data mining techniques. The need for QoS expertise is reduced as interactive visualization by brushing and linking of datasets reveals interrelation of parameters. Data contextualization by annotated data can aid the assessment on the global level of compatibility between supply and use conditions. Data dashboards can further simplify the analysis of QoS data by recognizing the network connectivity of different sites, seasonal effects and direction of voltage waveform events | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Data dashboards | en_US |
dc.subject | QoS | en_US |
dc.subject | PQ | en_US |
dc.subject | Data mining | en_US |
dc.subject | SQL | en_US |
dc.subject | QoS reporting | en_US |
dc.subject | Compliance to compatibility | en_US |
dc.subject | Network risk management | en_US |
dc.subject | Interactive data visualization | en_US |
dc.subject | Contextualization | en_US |
dc.title | Quality of supply data mining | en_US |
dc.type | Presentation | en_US |
dc.contributor.researchID | 10200029 - Rens, Abraham Paul Johannes | |