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Research paper | Open Access

Anomaly data management and big data analytics: an application on disability datasets

Zhiwen Pan1( ), Wen Ji1, Yiqiang Chen1, Lianjun Dai2, Jun Zhang2
Institute of Computing Technology Chinese Academy of Sciences, Beijing, China
Information Centre of China Disabled Persons’ Federation, Beijing, China
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Abstract

Purpose

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.

Design/methodology/approach

In this paper, the authors proposed a big data management and analytic approach for disability datasets.

Findings

By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.

Originality/value

The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

Keywords

Data analyticsDecision support systemsAnomaly data detectionData management systems

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International Journal of Crowd Science
Volume 2 Issue 2,
November 2018
Pages 164-176
DOI: 10.1108/IJCS-09-2018-0020
Cite this article:
Pan Z, Ji W, Chen Y, et al. Anomaly data management and big data analytics: an application on disability datasets. International Journal of Crowd Science, 2018, 2(2): 164-176. https://doi.org/10.1108/IJCS-09-2018-0020

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Received: 08 September 2018
Revised: 12 October 2018
Accepted: 13 October 2018
Published: 13 November 2018
© The author(s)

Zhiwen Pan, Wen Ji, Yiqiang Chen, Lianjun Dai and Jun Zhang. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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