Shaoyu Liang* Pages 20 - 28 ( 9 )
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.
Trajectory compression, map reduce, GPS trajectory, computing task, (k-Means) algorithm, trajectory compression algorithm.
Department of Information Engineering, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, 511300