| Title |
Smart City Data Storage Optimization in the Cloud |
| ID_Doc |
45620 |
| Authors |
Alkhelaiwi, A; Grigoras, D |
| Title |
Smart City Data Storage Optimization in the Cloud |
| Year |
2018 |
| Published |
|
| DOI |
10.1109/BigDataService.2018.00030 |
| Abstract |
Crowdsensing for smart city applications utilizes cloud computing to send to, store and publish data. The amount of data stored in the cloud is large and this introduces cost and storage challenges, since the cloud has a pay-as-you-use model. In this paper, we propose, as part of the data management architecture, a partitioning method that helps the user examine the flexibility of data in order to decide which of the reduction services can be applied on data. The results of the proposed partitioning method and its associated services show a large amount of storage savings in the cloud, from 24% to more than 30% in our analysis. |
| Author Keywords |
crowdsensing; partitioning; big data; smart city; storage optimization |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000593974100021 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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