Vol.8, No.4, November 2019.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Predicting the Number of Downloads of Open Datasets by Naïve Bayes Classifier


Barbara Šlibar


© 2019 Barbara Šlibar, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 8, Issue 4, Pages 1331-1338, ISSN 2217-8309, DOI: 10.18421/TEM84-33, November 2019.


Received: 21 August 2019.

Revised:   02 November 2019.
Accepted:  07 November 2019.
Published: 30 November 2019.




Nowadays, the use of Open Data has become more common and prominent, but there are a lot of questions regarding its quality. Most of the revised researches deal with the quality of Open Data portals, rather than estimation of the open datasets quality. Therefore, the main idea of this research is lowering to the level of the dataset itself in order to assess how much such data is downloaded by end users of Open Data portals on the basis of general dataset characteristics. A model for predicting the number of downloads of open datasets based on their general characteristics was constructed using the Naïve Bayes Classifier. Based on the obtained results, it is discussed if the certain dataset character is good predictor of open dataset downloading and to what extent.


Keywords – Dataset Characteristics; Naïve Bayes Classifier; Open Data.



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