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Role of Events R-Tree in Knowledge Discovery Process for Spatio-Temporal Databases

Affiliations

  • University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak – 124001, Haryana, India

Abstract


Objectives: The appraisal of data set in databases is expanding at a monster rate. These offers rise to a necessity for inventive systems and gadgets to help individuals in normally and astutely investigate gigantic data sets to gather profitable data. Here, the role of Events R-Trees in Knowledge Discovery Process for Spatio-Temporal Databases is presented. Methods: Taking into account the execution of R-tree, the thoughts of scope and cover are vital. Scope of a level of an R-tree is described as the total range of the extensive number of rectangles associated with the hubs of that level. Cover of a stage of an R-tree is described as the total zone contained inside at least two hubs. Obviously, powerful R-tree seeking demands that together cover and scope be limited. Findings: In this division, outfit a portrayal of KDD as well as Data Mining, recitation its assignments, techniques, along with correlations. In this paper, the role of Events R-Trees for powerful Knowledge Discovery Process for Spatio-Temporal Databases is explained. Improvements: The R-tree is a standout amongst the most referred to spatial information structures and it is all the time utilized for correlation with new structures.

Keywords

Events R-Tree, GSA (Gravitational Search Optimization Algorithms), KDD (Knowledge Discovery in Database), Patterns, Spatio-Temporal Database.

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References


  • Nandal R, Rishi R. Gravitational Search-Based Effective Knowledge Discovery Process for Spatio Temporal Databases. International Journal of Intelligent Engineering and Systems. 2016 Dec; 9(4):59–68. Crossref
  • Jin P, Xie X, Wang N, Yue L. Optimizing R-tree for flash memory. Journal of Expert Systems with Applications. 2015 Jan; 42(10):4676–86. Crossref
  • Tian Y, Rhodes P. A location service for partial spatial replicas implementing an R-tree in a relational database. Journal of Parallel and Distributed Computing. 2016 Jan; 90-91:9– 21. Crossref
  • Gamarra C, Guerrero J, Montero E. A knowledge discovery in databases approach for industrial micro grid planning. Journal of Renewable and Sustainable Energy Reviews. 2016 Jan; 60:615–30. Crossref
  • Gonzalez-Torres A, Garcia-Penalvo F, Theron-Sanchez R, Colomo-Palacios R. Knowledge discovery in software teams by means of evolutionary visual software analytics. Journal of Science of Computer Programming. 2016 Jun; 121:55–74. Crossref
  • Chaskalovic J, Assous F. Data mining and probabilistic models for error estimate analysis of finite element method. Journal of Mathematics and Computers in Simulation. 2016 Nov; 129:50–68. Crossref
  • Guttman A. R-trees: a dynamic index structure for spatial searching. ACM SIGMOD. 1984 Jun; 14(2):47–57. Crossref
  • Huang, Zhang L, Zhang P. A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets. Proceedings of the IEEE Transactions on Knowledge and Data Engineering. 2008 Apr; 20(4):433–48. Crossref

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