Main Reference PaperA Holistic Approach to Distributed Dimensionality Reduction of Big Data, IEEE Transactions on Cloud Computing, 2016 [Java/Hadoop].
  • An efficient method is proposed to only process the core amount of data from big data, because it have placed an unprecedented burden on current infrastructure. This work propose a Lanczos based High Order Singular Value Decomposition algorithm to reduce the dimensionality of the unified model. To execute the dimensionality reduction task, this paper employs the Transparent Computing paradigm to construct a distributed computing platform as well as utilizes the linear predictive model to partition the data blocks.

+ Description
  • An efficient method is proposed to only process the core amount of data from big data, because it have placed an unprecedented burden on current infrastructure. This work propose a Lanczos based High Order Singular Value Decomposition algorithm to reduce the dimensionality of the unified model. To execute the dimensionality reduction task, this paper employs the Transparent Computing paradigm to construct a distributed computing platform as well as utilizes the linear predictive model to partition the data blocks.

  • To improve the job running time.

  • Reducing computation cost and storage scheme.

+ Aim & Objectives
  • To improve the job running time.

  • Reducing computation cost and storage scheme.

  • An effective technique is contributed to reduce the unnecessary data storage on big data.

+ Contribution
  • An effective technique is contributed to reduce the unnecessary data storage on big data.

  • Java JDK 1.8, Hadoop 1.2.1

  • Netbeans 8.0.1, J2SE, Hadoop

+ Software Tools & Technologies
  • Java JDK 1.8, Hadoop 1.2.1

  • Netbeans 8.0.1, J2SE, Hadoop

  • B.E / B.Tech / M.E / M.Tech

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

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