American Journal of Software Engineering and Applications

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An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II

Received: 28 March 2013    Accepted:     Published: 02 April 2013
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Abstract

Predicting values at data points in a specified region when only a few values are known is a perennial problem and many approaches have been developed in response. Interpolation schemes provide some success and are the most widely used among the approaches. However, none of those schemes incorporates historical aspects in their formulae. This study presents an approach to interpolation, which utilizes the historical relationships existing between the data points in a region of interest. By combining the historical relationships with the interpolation equations, an algorithm for making predictions over an entire domain area, where data is known only for some random parts of that area, is presented. A performance analysis of the algorithm indicates that even when provided with less than ten percent of the domain’s data, the algorithm outperforms the other popular interpolation algorithms when more than fifty percent of the domain’s data is provided to them.

DOI 10.11648/j.ajsea.20130202.13
Published in American Journal of Software Engineering and Applications (Volume 2, Issue 2, April 2013)
Page(s) 40-48
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Modeling, Interpolation, Data Prediction, Sparse Data Analysis

References
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[5] W.C.M. Van Beers, J. P.C. Kleijnen, Kriging Interpolation in Simulation . A Survey, in. R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters (Eds.), Proceedings of the 2004 Winter Simulation Conference,Washington, DC, 2004, pp. 113-121.
[6] D. T. Lee, B.J. Schachter, Two Algorithms for Constructing a Delaunay Triangulation. International Journal of Computer and information Sciences, Vol. 9, 1980, pp. 219-242.
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[9] D. Shepard, A two-dimensional interpolation function for irregularly spaced data. Proceedings of the 23rd ACM National Conference (128), 1968, pp.517-524.
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Author Information
  • Dept. of Computer Science, Mathematics & Physics, Faculty of Science and Technology, University of the West Indies, Cave Hill Campus, Bridgetown, Barbados BB11000

  • Dept. of Computer Science, Mathematics & Physics, Faculty of Science and Technology, University of the West Indies, Cave Hill Campus, Bridgetown, Barbados BB11000

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  • APA Style

    John Charlery, Chris D. Smith. (2013). An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II. American Journal of Software Engineering and Applications, 2(2), 40-48. https://doi.org/10.11648/j.ajsea.20130202.13

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    ACS Style

    John Charlery; Chris D. Smith. An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II. Am. J. Softw. Eng. Appl. 2013, 2(2), 40-48. doi: 10.11648/j.ajsea.20130202.13

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    AMA Style

    John Charlery, Chris D. Smith. An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II. Am J Softw Eng Appl. 2013;2(2):40-48. doi: 10.11648/j.ajsea.20130202.13

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  • @article{10.11648/j.ajsea.20130202.13,
      author = {John Charlery and Chris D. Smith},
      title = {An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II},
      journal = {American Journal of Software Engineering and Applications},
      volume = {2},
      number = {2},
      pages = {40-48},
      doi = {10.11648/j.ajsea.20130202.13},
      url = {https://doi.org/10.11648/j.ajsea.20130202.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajsea.20130202.13},
      abstract = {Predicting values at data points in a specified region when only a few values are known is a perennial problem and many approaches have been developed in response. Interpolation schemes provide some success and are the most widely used among the approaches. However, none of those schemes incorporates historical aspects in their formulae.  This study presents an approach to interpolation, which utilizes the historical relationships existing between the data points in a region of interest. By combining the historical relationships with the interpolation equations, an algorithm for making predictions over an entire domain area, where data is known only for some random parts of that area, is presented. A performance analysis of the algorithm indicates that even when provided with less than ten percent of the domain’s data, the algorithm outperforms the other popular interpolation algorithms when more than fifty percent of the domain’s data is provided to them.},
     year = {2013}
    }
    

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    AB  - Predicting values at data points in a specified region when only a few values are known is a perennial problem and many approaches have been developed in response. Interpolation schemes provide some success and are the most widely used among the approaches. However, none of those schemes incorporates historical aspects in their formulae.  This study presents an approach to interpolation, which utilizes the historical relationships existing between the data points in a region of interest. By combining the historical relationships with the interpolation equations, an algorithm for making predictions over an entire domain area, where data is known only for some random parts of that area, is presented. A performance analysis of the algorithm indicates that even when provided with less than ten percent of the domain’s data, the algorithm outperforms the other popular interpolation algorithms when more than fifty percent of the domain’s data is provided to them.
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