Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring
文献类型：期刊论文
| 作者 | Balti, Hanen2,4; Ben Abbes, Ali2,3; Mellouli, Nedra4; Farah, Imed Riadh2,6; Sang, Yanfang1,5; Lamolle, Myriam4 |
| 刊名 | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 136页码:1-14 |
| 关键词 | Big data Data storage Spatio-temporal querying Decision-making Earth observation Disaster management |
| ISSN号 | 0167-739X |
| DOI | 10.1016/j.future.2022.05.010 |
| 通讯作者 | Balti, Hanen(hanen.balti@ensi-uma.tn) |
| 英文摘要 | The rapid increase in the number of Earth Observation (EO) systems generates a massive amount of heterogeneous data. It has raised big issues in collecting, preprocessing, storing, and the visualization these data. However, traditional techniques are facing serious challenges when dealing with big EO data dimensions (i.e., Volume, Veracity, Variety, and Velocity), especially in natural hazards management. Therefore, big data techniques and tools attract more attention. In this paper we propose a multidimensional model framework for Big EO data warehousing. This framework includes 3 parts: (1) Data collection and preprocessing, being responsible for collecting data and improving their quality; (2) Data loading and storage, performing the ingestion task which consists of transferring the data from external resources to the Big data platform for storage; and (3) Visualization and interpretation, aiming to provide spatio-temporal analysis. This framework could be useful for decision-makers in monitoring the effects of drought disasters and, consequently, planning the mitigation and remediation measures. Experiments are carried out on drought monitoring in China along the period 2000-2020. The input data include remote sensing data, biophysical data, and climatological data. The results reveal that the proposed framework has a higher retrieval speed and a greater elasticity with different kinds (i.e. spatial, temporal, or spatiotemporal) of requests compared to traditional frameworks, indicating its superiority. (C) 2022 Elsevier B.V. All rights reserved. |
| WOS关键词 | BIG DATA |
| 资助项目 | National Key Research and Development Program[2019YFA0606903] ; National Natural Science Foundation of China[41971040] ; CAS Interdisciplinary Innovation Team, China[JCTD-2019-04] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000884409700001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program ; National Natural Science Foundation of China ; CAS Interdisciplinary Innovation Team, China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187024]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Balti, Hanen |
| 作者单位 | 1.Minist Emergency Management China, Key Lab Compound & Chained Nat Hazards, Beijing 100085, Peoples R China 2.Ecole Natl Sci Informat, Lab RIADI, La Manouba 2010, Tunisia 3.FRB CESAB, F-34000 Montpellier, France 4.Univ Paris 08, Lab Intelligence Artificielle & Semant Donnees LI, Paris, France 5.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 6.IMT Atlantique, Lab ITI Dept, F-29238 Brest, France |
| 推荐引用方式 GB/T 7714 | Balti, Hanen,Ben Abbes, Ali,Mellouli, Nedra,et al. Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2022,136:1-14. |
| APA | Balti, Hanen,Ben Abbes, Ali,Mellouli, Nedra,Farah, Imed Riadh,Sang, Yanfang,&Lamolle, Myriam.(2022).Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,136,1-14. |
| MLA | Balti, Hanen,et al."Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 136(2022):1-14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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