Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics
| dc.contributor.author | Zeidan, Haitham | |
| dc.contributor.author | Najjar, Jad | |
| dc.contributor.author | Jayousi, Rashid | |
| dc.date.accessioned | 2021-07-14T12:11:37Z | |
| dc.date.available | 2021-07-14T12:11:37Z | |
| dc.date.issued | 2021-06-21 | |
| dc.description.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. | en_US |
| dc.identifier.citation | Haitham Zeidan, Jad Najjar, Rashid Jayousi. Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics. International Journal of Statistical Distributions and Applications. Vol. 7, No. 2, 2021, pp. 48-56. doi: 10.11648/j.ijsd.20210702.13 | en_US |
| dc.identifier.issn | 2472-3509 | |
| dc.identifier.uri | https://dspace.alquds.edu/handle/20.500.12213/6431 | |
| dc.language.iso | en | en_US |
| dc.publisher | Science Publishing Group | en_US |
| dc.subject | Hamming Distance | en_US |
| dc.subject | Edit (Levenshtein) Distance | en_US |
| dc.subject | Ontology | en_US |
| dc.subject | Algorithms | en_US |
| dc.subject | Interoperability | en_US |
| dc.subject | Visualization | en_US |
| dc.title | Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics | en_US |
| dc.type | Article | en_US |