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面向Spark的遥感影像金字塔模型的并行构建方法
  • ISSN号:1000-386X
  • 期刊名称:《计算机应用与软件》
  • 时间:0
  • 分类:TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
  • 作者机构:[1]上海海洋大学信息学院,上海201306
  • 相关基金:国家自然科学基金项目(61272098)
中文摘要:

海洋数据是一种典型的大数据,如何利用混合云存储架构存储海洋大数据是海洋数据管理面临的一个挑战.针对混合云存储架构中的关键问题-数据迁移,提出了海洋大数据的生命周期,并且基于此给出混合云存储中海洋大数据的迁移算法.在迁移算法中,将海洋数据的敏感度、数据访问频率、数据大小、数据时间长度等因素作为迁移因子.迁移算法兼顾考虑了数据存储容量、海洋数据本身的属性特征和数据访问过程中的动态变化.实验验证混合云存储模式能大大降低数据管理成本,同时,通过提出的迁移算法保证了数据的访问速度.

英文摘要:

Marine data is a typical big data. As the diversified marine data acquisition methods develop rapidly (remote sensing, GPS sensing, buoy monitoring, seabed monitoring, research ships and so on), the marine data grows explosively. However, the management of big data is a sophisticated problem currently. Cloud storage is an effective way to manage the big data. As the big marine data is characterized by massive, multisource, uncertain and especially the sensitive, the conventional architecture of cloud storage is not suitable for it. The big marine data is managed on hybrid cloud storage platform based on its characteristics and applications. Then, how to take advantage of hybrid cloud storage to manage the big marine data becomes a challenge. Data migration is the key question in the hybrid cloud storage. To resolve it, the lifecycle of big marine data is put forward, and based on it, the migration algorithm for the hybrid cloud storage is proposed. In the migration algorithm, the marine data sensitivity, data access frequency, data time length, data size and so on, are provided as the migration factors. Migration algorithm considers the capacity of data storage, attributes characteristics of marine data as well as the dynamic changes in the process of data access. The experimental results show that hybrid cloud storage pattern reduces the costs of managing big marine data greatly. Meanwhile, the access speed of big marine data is ensured by the presented migration algorithm.

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期刊信息
  • 《计算机应用与软件》
  • 北大核心期刊(2011版)
  • 主管单位:上海科学院
  • 主办单位:上海市计算技术研究所 上海计算机软件技术开发中心
  • 主编:朱三元
  • 地址:上海市愚园路546号
  • 邮编:200040
  • 邮箱:cas@sict.stc.sh.cn
  • 电话:021-62254715 62520070-505
  • 国际标准刊号:ISSN:1000-386X
  • 国内统一刊号:ISSN:31-1260/TP
  • 邮发代号:4-379
  • 获奖情况:
  • 全国计算机类中文核心期刊
  • 国内外数据库收录:
  • 波兰哥白尼索引,美国剑桥科学文摘,中国中国科技核心期刊,中国北大核心期刊(2004版),中国北大核心期刊(2011版),中国北大核心期刊(2000版)
  • 被引量:27463