Main Reference PaperK Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis, IEEE Transactions on Knowledge and Data Engineering, 2016 [Java/Hadoop].
  • This work surveys the existing methods of kNN in MapReduce, and compares their performance. It analyzes 5 algorithms such as H-BkNNJ, H-BNLJ, PGBJ, H-zkNNJ and RankReduce. To compute kNN over MapReduce, it involves three steps. 1) data preprocessing 2) data partitioning 3) KNN computation, MapReduce jobs depend on the number of distances required to be calculated and sorted.

+ Description
  • This work surveys the existing methods of kNN in MapReduce, and compares their performance. It analyzes 5 algorithms such as H-BkNNJ, H-BNLJ, PGBJ, H-zkNNJ and RankReduce. To compute kNN over MapReduce, it involves three steps. 1) data preprocessing 2) data partitioning 3) KNN computation, MapReduce jobs depend on the number of distances required to be calculated and sorted.

  • To analyze the KNN performance using communication overhead, communication overhead and Execution time.

+ Aim & Objectives
  • To analyze the KNN performance using communication overhead, communication overhead and Execution time.

  • A technique is contributed to improve the KNN scheme.

+ Contribution
  • A technique is contributed to improve the KNN scheme.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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