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

John Charlery; Chris D. Smith

doi:doi:10.11648/j.ajsea.20130202.13

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

John Charlery, Chris D. Smith

Published in American Journal of Software Engineering and Applications (Volume 2, Issue 2)

Published: 2 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.

Published in	American Journal of Software Engineering and Applications (Volume 2, Issue 2)
DOI	10.11648/j.ajsea.20130202.13
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), 2013. Published by Science Publishing Group

Keywords

Data Modeling, Interpolation, Data Prediction, Sparse Data Analysis

References

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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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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II
AU  - John Charlery
AU  - Chris D. Smith
Y1  - 2013/04/02
PY  - 2013
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DO  - 10.11648/j.ajsea.20130202.13
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 40
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PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20130202.13
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.
VL  - 2
IS  - 2
ER  -

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Author Information

John Charlery

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

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - An Approach to Modeling Domain-Wide Information, based on Limited Points’ Data – Part II
AU  - John Charlery
AU  - Chris D. Smith
Y1  - 2013/04/02
PY  - 2013
N1  - https://doi.org/10.11648/j.ajsea.20130202.13
DO  - 10.11648/j.ajsea.20130202.13
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 40
EP  - 48
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20130202.13
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.
VL  - 2
IS  - 2
ER  -

Copy | Download