Taking a ‘Big Data’ approach to data quality in a citizen science project

a school of environmental science and engineering, chang'an university, Xi 'an, china; b Key laboratory of subsurface Hydrology and ecological effects in arid region of the ministry of education, chang'an university, Xi'an, china ABSTRACT Water resources are the basis for the sustainable development of China. However, the country is currently facing alarming waterrelated problems associated with its fast economic development and climate change. This editorial introduces briefly the status of its water resources research at the national level and the researcher level. It also introduces the research reported in this thematic issue to highlight its role in addressing flood and water-scarcity issues, improving water management in inland plains and supporting the sustainable development of the country. Finally, some further recommendations for improving water resources research in China are offered.

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“…However, this is only a narrow collaboration, which may not be very successful in a Big Data era (Kelling et al, 2015).…”

Section: Expanding Professional Collaboration To Citizen Sciencementioning

confidence: 99%

Water resources research to support a sustainable China

Qian

2018

International Journal of Water Resources Development

202

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“…First is the authenticity of (big) data collecting method, which is rather opportunistic than scientific [17][18][19]. This factor may significantly affect representativeness of data sample being used for analysis.…”

Section: B Literature Reviewmentioning

confidence: 99%

Antecedents of big data quality: An empirical examination in financial service organizations

Haryadi

Hulstijn

Wahyudi

et al. 2016

2016 IEEE International Conference on Big Data (Big Data)

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Abstract-Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.

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“…In [43] Hossain, et al dynamically assess three quality attributes for the detection and identification of human presence in multimedia monitoring systems, where-as Rodríguez and Riveill [44] present data quality in relation to e-Health monitoring systems. Crowd-sourced citizen science [45] and volunteered geographic information [46][47][48] efforts attract data quality research for obvious reasons. When the public assists in collecting data, the benefits of public collection must be weighed against the potential for poor quality submissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatio-temporal quality control: implications and applications for data consumers and aggregators

Galarus

Angryk

2016

Open geospatial data, softw. stand.

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Background: Quality control for real-time spatio-temporal data is often presented from the perspective of the original owner and provider of the data, and focuses on general techniques for outlier detection or uses domain-specific knowledge and rules to assess quality. The impact of quality control on the data aggregator and redistributor is neglected. As sensor networks proliferate, multiple providers can distribute and redistribute the same original sensor data. Relationships between providers become complex, with data acquired from original and third-party sources. One provider may acquire data from another, and so forth, resulting in larger data sets with value-added components such as quality indicators, but with costs such as increased lag between original observation times and (re)distribution times. Methods: The focus of this paper is to define and demonstrate quality control measures for real-time, spatio-temporal data from the perspective of an aggregator to provide tools for evaluation and comparison of overlapping, real-time, spatio-temporal data providers and for assessment and optimization of data acquisition, system operation and data redistribution. We define simple measures that account for temporal completeness and spatial coverage. The measures and methods developed are tested on real-world data and applications. Results: Our results show that these simple measures combine to form methods that are useful in comparing providers and identifying patterns in data which can then be exploited to optimize system performance relative to bandwidth, and to assess the impact of provider quality control mechanisms. Conclusion:The simple measures presented demonstrate the utility of quantifying data quality from the perspective of the data aggregator and redistributor.

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Taking a ‘Big Data’ approach to data quality in a citizen science project

Cited by 140 publications

References 31 publications

Water resources research to support a sustainable China

Water resources research to support a sustainable China

Antecedents of big data quality: An empirical examination in financial service organizations

Spatio-temporal quality control: implications and applications for data consumers and aggregators

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