云平台中可优化分析操作的海量医学图像存储管理技术

项目名称： 云平台中可优化分析操作的海量医学图像存储管理技术

项目编号： No.61462012

项目类型： 地区科学基金项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 李晖

作者单位： 贵州大学

项目金额： 45万元

中文摘要： 现有的医学图像存储技术，对基于医学图像的分析型任务提供的性能优化支持较为有限。在本项目中，将针对此问题，研究新型存储管理技术。包括： （1）基于闪存设备的物理新特性，研究适用于云环境的行列混合存储技术。通过引入行列混合存储自适应机制，提供面向列的存储模式，解决医学影像数据存储管理系统由于没有利用存储模式的特点，从而较难高效的对分析型任务提供性能优化支持的问题； （2）对于以列模式存储的医学影像数据，通过研究可预计算的距离度量，及相应的剪枝规则，设计出能同时降低I/O和计算开销的分布式索引技术。通过该索引进行数据访问和数据处理时，医学图像的分析型任务处理过程中的I/O和计算开销，将被有效减少； （3）对于以行模式存储的医学影像相关数据，研发分布式索引技术，解决数据连续的以高速进入系统时，现有索引技术结点分裂频繁、I/O和计算开销大、无法为数据存储系统实时的构建和维护索引的问题。

中文关键词： 海量数据管理；数据存储；非结构化数据；分布式索引；云计算

英文摘要： Existing medical images storage techniques often cannot efficiently support optimization for massive medical images based anlaytical tasks. To solve this problem, we conduct research in this projcet in following issues: (1) Based on the physical characterics of flash devices, we propose a cloud based hybrid (row-column) storage technique. Through utilize the column based data sotrage schema, and benefit from the advantage that coloumn storage often has significanly less I/O overheads, we can solve the problem that conventional medical images storage cannot opitimze the performance of medical images based analytical tasks. (2) Towards the medical images stored in coloumn based schema, we devise several precomputed distance metrics and its corresponding pruning rules, and integrate them into a newly designed R-Tree based distributed multidimensional index. Then the index can reduce both I/O and computational overheads at the same time. By using this indexing technique for data access and analysis, various cost will significanlty decreased. (3) As to the fast and continuously arriving massive medical images related data which stored in row based schema, we propose a distributed append-efficient multidimensional index to quickly archive and indexing incoming data. It employ a newly designed data insertion algorithm, whose node split strategy is significantly different form traditional R-Tree like techniques due to we revise some basic rules and inherent characteristics of R-Tree, and abandon the optimization for update, due to the update is not necessary for medical data during its lifecycle. Benefit form this design, this index will rarely get node split and hence it will have only a litter bit I/O and computational cost during indexing. This means it can be utilized to replace traditional mecical image storage system, which often involve expensive node split, heavy I/O and computational overheads, and cannot archive and indexing the continuously arriving data in near real-time. The study of this project will lead several output of cloud based medical images storage techniques, it not only useful to meical image storage managemnt, but also applicable to many unstructured or multimeda data domain. Therefore, the techniques proposed in this proposal will have big potential commercial value.

英文关键词： Massive Data Management;Data Storage;Unstructured Data;Distributed Indexing;Cloud Computing