par Mateus, Rodrigo Costa;Lopes Siqueira, Thiago Luis 
;Times, Valéria Cesário;Ciferri, Ricardo Rodrigues;de Aguiar Ciferri, Cristina Dutra de Aguiar C.D.d.A.
Référence Distributed and parallel databases, 34, 3, page (425-461)
Publication Publié, 2016-09
;Times, Valéria Cesário;Ciferri, Ricardo Rodrigues;de Aguiar Ciferri, Cristina Dutra de Aguiar C.D.d.A.Référence Distributed and parallel databases, 34, 3, page (425-461)
Publication Publié, 2016-09
Article révisé par les pairs
| Résumé : | Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %. |
