Main Reference PaperReducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data, IEEE International Congress on Big Data, 2014.
  • The data is searched from huge amount of uncertain data like finding users interesting patterns from big data where data are in thousands of terabytes. To avoid wasting of a lot of time and space in searching the frequent patterns from uncertain Big data, this paper allows users to express their interest in terms of succinct anti-monotone (SAM) constraints and employs MapReduce to mine uncertain Big data for frequent patterns that satisfy the user-specified constraints.

+ Description
  • The data is searched from huge amount of uncertain data like finding users interesting patterns from big data where data are in thousands of terabytes. To avoid wasting of a lot of time and space in searching the frequent patterns from uncertain Big data, this paper allows users to express their interest in terms of succinct anti-monotone (SAM) constraints and employs MapReduce to mine uncertain Big data for frequent patterns that satisfy the user-specified constraints.

  • To achieve the space-efficient and time efficient in search the interesting pattern from uncertain data.

  • To reduce the computation by apply the constraints on map function.

+ Aim & Objectives
  • To achieve the space-efficient and time efficient in search the interesting pattern from uncertain data.

  • To reduce the computation by apply the constraints on map function.

  • To further reduce the search space and execution time in uncertain Big data, this work provides importance to frequency of items using weighting factors. It can reduce the nodes in the first level of tree, which leads to reduction in the size of the tree and execution time.

+ Contribution
  • To further reduce the search space and execution time in uncertain Big data, this work provides importance to frequency of items using weighting factors. It can reduce the nodes in the first level of tree, which leads to reduction in the size of the tree and execution time.

  • Java JDK 1.8, Hadoop 1.2.1

  • Netbeans 8.0.1, Hadoop

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

  • Netbeans 8.0.1, Hadoop

  • 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.

  • No Readymade Projects-Depending on the complexity of the project and requirements.

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

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