Main Reference PaperA Holistic Approach to Distributed Dimensionality Reduction of Big Data, IEEE Transactions on Cloud Computing, 2018 [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 the current computing infrastructure. This work proposes 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 the current computing infrastructure. This work proposes 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 unnecessary data storage is reduced on big data

Contribution
  • An unnecessary data storage is reduced on big data

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

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Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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