Background: Mass movement trajectory data with real scenarios has been evolved with
big data mining to solve the data redundancy problem.
Methods: This paper proposes a parallel path based on the Map Reduce compression method, using
two kinds of piecewise point mutual crisscross, the classified method of trajectory, and then segment
trajectory distribution to multiple nodes to parallelize the compression.
Results: Finally, the results based on both compression methods have been simulated for the different
real-time data by merging both techniques.
Conclusion: The performance test results show that the parallel trajectory compression method proposed
in this paper can greatly improve the compression efficiency and completely eliminate the
error caused by the failure of the correlation between the segments.