On June 21, 2021, Dr. Rashid Jayousi published a new study in the International Journal of Statistical Distributions and Applications titled Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. The article aimed to introduce a new interoperable visual analytics framework towards enhancing presentation of Official Statistics and to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. The results of this study demonstrated that new proposed mapping algorithm based on hamming distance, edit distance and ontology, can enhance integration and mapping of statistical data indicators from different sources, the data after importing is saved in the schema created, the schema included ontology tables to improve and increase the accuracy of the mapping algorithm. The experimental results showed high accuracy of mapping for the algorithm by adding the ontology to the algorithm. the accuracy of the algorithm when importing and mapping indicators without ontology was 67% and with ontology the accuracy was 89%, for units mapping the accuracy was 82% without ontology and 95% with ontology, and for subgroups the accuracy was 78% without ontology and 100% with ontology. In general, we can conclude that adding the ontology to the new algorithm in addition to using of hamming distance and edit distance improved the algorithm accuracy for mapping indicators, units and subgroups.
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