Abstract: As a new strategic resource,big data plays an important role in the field of information.The scale of big data retrieval often reaches billions or even ten billions,resulting in that traditional query mechanism’s low efficiency becomes regular.Therefore,improving the efficiency of big data query and reducing the burden of querying big data have become an important aspect of big data research.In order to speed up the querying of big data as well as reduce the burden,we proposed a big data retrieval filtering model IMFM of batch-oriented processing,demonstrated its support for multi-dimensional queries,and gave out the IMFM’s deployment strategy.By deploying the model in the appropriate position of the index structure,IMFM can filter the search requests that pass through the node quickly to avoid that the lower node is searched,so as to reduce the consumption of retrieval performance.Experiments show that,in the batch-oriented processing of big data environment,IMFM can effectively reduce the path length of both single and multi-dimensional data queries,improve the efficiency of retrieval and reduce the workload of big data storage and processing platform significantly.

Key words: Big data,Retrieve,Filter,Index architecture,Multi-dimensional query