Publication
BI and data warehouse solutions for energy production industry: application of the CRISP-DM methodology.
| dc.contributor.author | Mendes, Armando B. | |
| dc.date.accessioned | 2013-07-23T11:31:13Z | |
| dc.date.available | 2013-07-23T11:31:13Z | |
| dc.date.issued | 2010-07 | |
| dc.date.updated | 2013-07-18T17:13:22Z | |
| dc.description.abstract | This paper reports two projects for supporting decisions of the Company of Electricity in Azores Islands, Electricidade dos Açores. There were several decisions to support, such as whether communications between islands should moved from the present telephone lines to VoIP, and if better models to support forecast power consumption should be adopted. The solution established integrates OLAP cubes in a data mining project, based on CRISP-DM process model. Both for strategic and more operational decisions the objective was always to get accurate data, build a data warehouse and to get tools to analyze it in order to properly inform the decision makers. These DSS's translates big CSV flat files or acquire data in real time from operational Data Bases to update a data warehouse, including importing, evaluating data quality and populating relational tables. Multidimensional data cubes with numerous dimensions and measures were used for operational decisions and as exploration tools in the strategic ones. Data mining models for forecasting, clustering, decision trees and association rules identified several inefficient procedures and even fraud situations. Not only was possible to support the necessary decisions, but several models were also displayed so that control decision makers and strategists could support new problems. | en |
| dc.identifier.citation | Mendes, Armando B. (2010). "BI and data warehouse solutions for energy production industry: application of the CRISP-DM methodology". In Respício, A.; Adam, F.; Phillips-Wren, G.; Teixeira, C. e Telhada, J. (Eds.), «Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the next decade», Series 'Frontiers in Artificial Intelligence and Applications', vol. 212. IOS Press, pp. 211-222. ISBN: 978-1-60750-576-1. | en |
| dc.identifier.isbn | 978-1-60750-576-1 (Print) | |
| dc.identifier.uri | http://hdl.handle.net/10400.3/2150 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | IOS Press | por |
| dc.relation.publisherversion | http://dx.doi.org/10.3233/978-1-60750-577-8-211 | por |
| dc.subject | Decision Support Systems | en |
| dc.subject | Data Mining | en |
| dc.subject | OLAP | por |
| dc.subject | Operations Efficiency | en |
| dc.title | BI and data warehouse solutions for energy production industry: application of the CRISP-DM methodology. | en |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Amsterdam, Netherlands | por |
| oaire.citation.endPage | 222 | por |
| oaire.citation.startPage | 211 | por |
| oaire.citation.title | Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the next decade | en |
| oaire.citation.volume | 212 | por |
| rcaap.rights | openAccess | por |
| rcaap.type | bookPart | por |
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