Research on the Method and Application of MapReduce in Mobile Track Big Data Mining

Title:Research on the Method and Application of MapReduce in Mobile Track Big Data Mining

Volume: 14 Issue: 1

Author(s): Shaoyu Liang*

Affiliation:

• Department of Information Engineering, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, 511300,China

Keywords: Trajectory compression, map reduce, GPS trajectory, computing task, (k-Means) algorithm, trajectory compression algorithm.

Abstract:

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.

Cite this article as:

Liang Shaoyu *, Research on the Method and Application of MapReduce in Mobile Track Big Data Mining, Recent Advances in Electrical & Electronic Engineering 2021; 14 (1) . https://dx.doi.org/10.2174/2352096513999200720165146

DOI https://dx.doi.org/10.2174/2352096513999200720165146	Print ISSN 2352-0965
Publisher Name Bentham Science Publisher	Online ISSN 2352-0973