Distributed Multidimensional Data Indexing Strategy in Cloud Computing Environment

摘要

Distributed multidimensional data index is an important tool to realize massive data retrieval task. However, traditional data index ignores the influence of distributed data offset on index structure, which results in the large number of index iterations and long retrieval time. The distributed multi-dimensional data index strategy in cloud computing environment is studied. In the cloud computing environment, parallel processing of offset distributed data; through differentiation of different data dimensions, the establishment of distributed multidimensional data index structure; the construction of node selection cost detection model, through adjusting the local node selection location, optimizing index retrieval structure, to achieve the distributed multidimensional data retrieval task. Experimental results: the number of iterations of the index is 251 times and 278 times lower than the two groups of traditional indexes, and the retrieval time is 6.91s and 6.8034s lower than the two traditional indexes. It can be seen that the index strategy in cloud computing environment has better data retrieval performance.