To address the low efficiency problem caused by the approximate large-scale high-dimensional data query

we propose a novel high-dimensional index and KNN query method

called iPBM

which exploits two main problems

including the optimal division on the MapReduce's data block and their contributions to the computing.Specifically

based on the principles of relativity and parallelity

iPBM employs a two-phase partitioning scheme of clustering and zoning to equally split the data to the available blocks

then we design a distributed two-layer index structure and parallel KNN query algorithm.With fully considering the global index

local index and two-dimensional bitcode property

iPBM achieves triple-layers filtering

and thus the number of queried area and the computing cost on the high-dimensional data is minimized.The accuracy

efficiency and scalability of the proposed iPBM are thoroughly evaluated via detailed simulations.