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https://hdl.handle.net/1822/46382
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Campo DC | Valor | Idioma |
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dc.contributor.author | Costa, Carlos | por |
dc.contributor.author | Santos, Maribel Yasmina | por |
dc.date.accessioned | 2017-09-08T09:04:16Z | - |
dc.date.issued | 2017-07 | - |
dc.identifier.citation | Costa, Carlos, and Maribel Yasmina Santos, “The SusCity Big Data Warehousing Approach for Smart Cities”. In Proceedings of International Database Engineering & Applications Symposium (IDEAS’17), Bristol, United Kingdom, 12-14 July, 2017, pp. 264-273. DOI: 10.1145/3105831.3105841 | por |
dc.identifier.isbn | 9781450352208 | por |
dc.identifier.uri | https://hdl.handle.net/1822/46382 | - |
dc.description.abstract | Nowadays, the concept of Smart City provides a rich analytical context, highlighting the need to store and process vast amounts of heterogeneous data flowing at different velocities. #is data is defined as Big Data, which imposes significant difficulties in traditional data techniques and technologies. Data Warehouses (DWs) have long been recognized as a fundamental enterprise asset, providing fact-based decision support for several organizations. #e concept of DW is evolving. Traditionally, Relational Database Management Systems (RDBMSs) are used to store historical data, providing different analytical perspectives regarding several business processes. With the current advancements in Big Data techniques and technologies, the concept of Big Data Warehouse (BDW) emerges to surpass several limitations of traditional DWs. #is paper presents a novel approach for designing and implementing BDWs, which has been supporting the SusCity data visualization platform. #e BDW is a crucial component of the SusCity research project in the context of Smart Cities, supporting analytical tasks based on data collected in the city of Lisbon. | por |
dc.description.sponsorship | This work has been supported by COMPETE: POCI-01-0145- FEDER- 007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013, and the SusCity project, MITP-TB/CS/0026/2013. | por |
dc.language.iso | eng | por |
dc.publisher | Association for Computing Machinery (ACM) | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5665-PICT/137220/PT | por |
dc.rights | restrictedAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Big Data | por |
dc.subject | Big Data Warehousing | por |
dc.subject | Hadoop | por |
dc.subject | NoSQL | por |
dc.subject | Data Warehouse | por |
dc.subject | Smart Cities | por |
dc.title | The SusCity big data warehousing approach for smart cities | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 264 | por |
oaire.citationEndPage | 273 | por |
oaire.citationVolume | Part F129476 | por |
dc.identifier.doi | 10.1145/3105831.3105841 | por |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
sdum.conferencePublication | Proceedings of International Database Engineering & Applications Symposium (IDEAS’17) | por |
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IDEAS_2017_CC_MYS.pdf Acesso restrito! | 2,09 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons