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MapReduce Algorithms: Consecutive Retrieval of Clusters and Blackboard Database System

EasyChair Preprint no. 3326

9 pagesDate: May 4, 2020


The objects of data mining are knowledge discovery process and reduce time complexity. Time taken for Information retrieval in big data is very high. Time complexity will be reduced through information retrieval techniques.  Cluster is set of query-data item instances. Consecutive Retrieval(C-R) cluster Property is retrieval of data items in data set or cluster   from the consecutive locations. This may be achieved through the consecutively retrieval (C-R) cluster property.   C-R cluster property is retrieval information using query-data set incidence or clusters.  MapReduce algorithms are Map and Reduce for cluster retrieval consecutively. The time will be reduced through the consecutive retrieval cluster property. Parallelism of clusters is designed through parallel clusters, distributed and concurrency of clusters. The parallel clusters are designed using vector approach and genetic algorithms approach.  The distributed and parallel algorithms are designed through blackboard architecture. Time and space complexity shall be reduced using directly storage data items with the Blackboard Architecture.   The blackboard architecture shall be used store and retrieve the data items of clusters.

Keyphrases: Blackboard Architecture, Blackboard database systems, cluster analysis, Consecutive Retrieval, Data Mining, MarReduce algorithms

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Venkata Subba Reddy Poli},
  title = {MapReduce Algorithms: Consecutive Retrieval of Clusters and Blackboard Database System},
  howpublished = {EasyChair Preprint no. 3326},

  year = {EasyChair, 2020}}
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