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].
  • The aim of this work is to survey the existing methods of kNN in MapReduce, and to compare their performance. This work analyzes the 5 algorithms such as H-BkNNJ, H-BNLJ, PGBJ, H-zkNNJ and RankReduce. To compute kNN over MapReduce, it consistes of three steps. 1) data preprocessing 2) data partitioning 3) KNN computation: MapReduce jobs depending on the number of distances want to calculate and sort.

+ Description
  • The aim of this work is to survey the existing methods of kNN in MapReduce, and to compare their performance. This work analyzes the 5 algorithms such as H-BkNNJ, H-BNLJ, PGBJ, H-zkNNJ and RankReduce. To compute kNN over MapReduce, it consistes of three steps. 1) data preprocessing 2) data partitioning 3) KNN computation: MapReduce jobs depending on the number of distances want to calculate and sort.

  • 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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