An Assessment of Open Data Sets Completeness
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 6
Abstract
The rapid growth of open data sources is driven by free-of-charge contents and ease of accessibility. While it is convenient for public data consumers to use data sets extracted from open data sources, the decision to use these data sets should be based on data sets’ quality. Several data quality dimensions such as completeness, accuracy, and timeliness are common requirements to make data fit for use. More importantly, in many cases, high-quality data sets are desirable in ensuring reliable outcomes of reports and analytics. Even though many open data sources provide data quality guidelines, the responsibility to ensure data of high quality requires commitment from data contributors. In this paper, an initial investigation on the quality of open data sets in terms of completeness dimension was con-ducted. In particular, the results of the missing values in 20 open data sets measurement were extracted from the open data sources. The analysis covered all the missing values representations which are not limited to nulls or blank spaces. The results exhibited a range of missing values ratios that indicated the level of the data sets completeness. The limited coverage of this analysis does not hinder understanding of the current level of data completeness of open data sets. The findings may motivate open data providers to design initiatives that will empower data quality policy and guidelines for data contributors. In addition, this analysis may assist public data users to decide on the acceptability of open data sets by applying the simple methods proposed in this paper or performing data cleaning actions to improve the completeness of the data sets concerned.
Authors and Affiliations
Abdulrazzak Ali, Nurul A. Emran, Siti A. Asmai, Amelia R. Ismail
Keywords
Related Articles
- EP ID EP597485
- DOI 10.14569/IJACSA.2019.0100672
- Views 93
- Downloads 0
Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks
The paper proposes the dynamic modification of the activation function in a learning technique, more exactly backpropagation algorithm. The modification consists in changing slope of sigmoid function for activation funct...
Towards Effective Service Discovery using Feature Selection and Supervised Learning Algorithms
With the rapid development of web service technologies, the number and variety of web services available on the internet are rapidly increasing. Currently, service registries support human classification, which has been...
Anomaly Detection with Machine Learning and Graph Databases in Fraud Management
In this paper, the task of fraud detection using the methods of data analysis and machine learning based on social and transaction graphs is considered. The algorithms for feature calculation, outlier detection and ident...
Iterative Learning Control for Trajectory Tracking of Single-link Flexible Arm
This paper focuses on the issue of tracking the trajectory of a flexible arm. The purpose is to ensure the flexible arm follows the desired path in the joint space. To achieve our objective, we have three problems to sol...
Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine
Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology. Digital videos have become very...