针对当前云存储辅助多维索引中基于哈希结构的索引研究不足的现状,本项目拟研究并提出新型概率数据结构Ordinal bloom filter(OBF)，并对其性能进行建模分析与评价,使其能适应云存储动态变化的需求。以此为基础，在Hadoop存储系统中研究构建多维索引结构，该结构能够和Hadoop中MapReduce框架有效整合，提高Hadoop的检索效率，并便于维护。除此之外，本项目还尝试建立一套相对完整的评价模型（cost model），将这一模型作为后面一系列优化工作的基石。再针对不同的索引优化目标（如：索引的压缩、性能，空间利用率 等等）来开展研究工作。本课题针对Hadoop云环境下辅助多维索引的特性，提出新的理论、方法和解决方案。其关键技术的突破，对于未来云存储索引效率提高和海量数据过滤，以及一些重要理论问题，如概率哈希数据结构对云存储的动态适应性等做出有意义的提示。

The current research of auxiliary multi-dimensional Cloud storage index based on hash data structure is not enough. This project intends to propose a new probabilistic data structure called ordinal bloom filter(OBF) and analyze its performance, so that it can adapt to the dynamic changes in Cloud storage. Then we will investigate how to setup an auxiliary multi-dimensional Cloud storage index in Hadoop, and how to comprehensively integrate the multi-dimensional index into Hadoop's MapReduce framework. This index can enhance the Hadoop search performance and should be easy to maintain. Furthermore, This project also attempts to establish an evaluation model (cost model), based on which different research works (such as index compression, performance, space utilization etc.) will be carried out. This project aims to put forward new theories, methods and solutions for Hadoop multi-dimensional index. The key technologies breakthrough will have great significance for the future of Cloud storage index and mass data filtering, as well as some important theoretical issues, such as how the probabilistic data structures contribute to the Cloud storage dynamical adaptability.