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Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics

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.
Cite This Article
  • 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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    AU  - Haitham Zeidan
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    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
  • Palestinian Central Bureau of Statistics (PCBS), Jerusalem, Palestine

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

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

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