Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics

Haitham Zeidan; Jad Najjar; Rashid Jayousi

doi:doi:10.11648/j.ijsd.20210702.13

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Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics

Haitham Zeidan, Jad Najjar, Rashid Jayousi

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 2)

Received: 24 November 2020 Accepted: 18 December 2020 Published: 21 June 2021

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Abstract

The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 2)
DOI	10.11648/j.ijsd.20210702.13
Page(s)	48-56
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), 2021. Published by Science Publishing Group

Keywords

Hamming Distance, Edit (Levenshtein) Distance, Ontology, Algorithms, Interoperability, Visualization

References

[1]	Hamming Distance: http://en.wikipedia.org/wiki/Hamming_distance.
[2]	Levenstein, V. (1966), Binary Codes Capable of Correcting Deletions, Insertions, and Reversals. Sov. Phys. Dokl. 10: 707-710.
[3]	Palestinian Central Bureau of Statistics: http://www.pcbs.gov.ps.
[4]	Robert K. (2007). Visualization Criticism - The Missing Link Between Information Visualization and Art. 11th International Conference Information Visualization (IV '07). 10.1109/IV.2007.130.
[5]	Yacine, B.(2012). String Comparators Based Algorithms for Process Model Matchmaking. IEEE SCC. 10.1109/SCC.2012.69.
[6]	Gal A., Shvaiko P. (2008) Advances in Ontology Matching. In: Dillon T. S., Chang E., Meersman R., Sycara K. (eds) Advances in Web Semantics I. Lecture Notes in Computer Science, vol 4891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89784-2_6.
[7]	Schmitt, I., Saake, G. (2005) A comprehensive database schema integration method based on the theory of formal concepts. Acta Informatica 41, 475–524. https://doi.org/10.1007/s00236-005-0166-2.
[8]	Kalampokis, Evangelos & Karamanou, Areti & Tarabanis, Konstantinos. (2019). Interoperability Conflicts in Linked Open Statistical Data. Information. 10. 249. 10.3390/info10080249.
[9]	Rahm, E. and Bernstein, P. (2001), “A survey of approaches to automatic schema matching,” The VLDB Journal, vol. 10, no. 4, pp. 334–350.
[10]	Gal, A. and Shvaiko, P. (2009), “Advances in ontology matching,” in Advances in Web Semantics I, T. S. Dillon, E. Chang, R. Meersman, and K. Sycara, Eds. Springer, pp. 176–198.
[11]	Bellahsene, Z., Bonifati, A., and Rahm, E. (2011), Schema Matching and Mapping. Springer.
[12]	Michaela, D. and Peter, H. (2004), Data Integration: Techniques and Evaluation, The DIECOFIS Project: Progress and Lessons. Austrian Journal of Statistics.
[13]	Filippo, O. and Francesca, I. (2004), The Development of an Integrated and Systematized Information System for Economic and Policy Impact Analysis. Austrian Journal of Statistics, Volume 33 (2004), Number 1&2, 211-235.
[14]	Department of Statistics (Jordan): http://www.dos.gov.jo.
[15]	Central Agency for Public Mobilization and Statistics (Egypt): http://capmas.gov.eg.

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APA Style

Haitham Zeidan, Jad Najjar, Rashid Jayousi. (2021). Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. International Journal of Statistical Distributions and Applications, 7(2), 48-56. https://doi.org/10.11648/j.ijsd.20210702.13

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ACS Style

Haitham Zeidan; Jad Najjar; Rashid Jayousi. Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. Int. J. Stat. Distrib. Appl. 2021, 7(2), 48-56. doi: 10.11648/j.ijsd.20210702.13

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AMA Style

Haitham Zeidan, Jad Najjar, Rashid Jayousi. Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. Int J Stat Distrib Appl. 2021;7(2):48-56. doi: 10.11648/j.ijsd.20210702.13

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@article{10.11648/j.ijsd.20210702.13,
  author = {Haitham Zeidan and Jad Najjar and Rashid Jayousi},
  title = {Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {2},
  pages = {48-56},
  doi = {10.11648/j.ijsd.20210702.13},
  url = {https://doi.org/10.11648/j.ijsd.20210702.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210702.13},
  abstract = {The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.},
 year = {2021}
}

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TY  - JOUR
T1  - Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics
AU  - Haitham Zeidan
AU  - Jad Najjar
AU  - Rashid Jayousi
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210702.13
DO  - 10.11648/j.ijsd.20210702.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 48
EP  - 56
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210702.13
AB  - The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.
VL  - 7
IS  - 2
ER  -

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Author Information

Haitham Zeidan

Palestinian Central Bureau of Statistics (PCBS), Jerusalem, Palestine
Jad Najjar

Computer Science Department, Al-Quds University, Jerusalem, Palestine
Rashid Jayousi

Computer Science Department, Al-Quds University, Jerusalem, Palestine

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Plain Text BibTeX RIS

APA Style

Haitham Zeidan, Jad Najjar, Rashid Jayousi. (2021). Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. International Journal of Statistical Distributions and Applications, 7(2), 48-56. https://doi.org/10.11648/j.ijsd.20210702.13

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ACS Style

Haitham Zeidan; Jad Najjar; Rashid Jayousi. Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. Int. J. Stat. Distrib. Appl. 2021, 7(2), 48-56. doi: 10.11648/j.ijsd.20210702.13

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AMA Style

Haitham Zeidan, Jad Najjar, Rashid Jayousi. Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. Int J Stat Distrib Appl. 2021;7(2):48-56. doi: 10.11648/j.ijsd.20210702.13

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@article{10.11648/j.ijsd.20210702.13,
  author = {Haitham Zeidan and Jad Najjar and Rashid Jayousi},
  title = {Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {2},
  pages = {48-56},
  doi = {10.11648/j.ijsd.20210702.13},
  url = {https://doi.org/10.11648/j.ijsd.20210702.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210702.13},
  abstract = {The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics
AU  - Haitham Zeidan
AU  - Jad Najjar
AU  - Rashid Jayousi
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210702.13
DO  - 10.11648/j.ijsd.20210702.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 48
EP  - 56
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210702.13
AB  - The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.
VL  - 7
IS  - 2
ER  -

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