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

Abstract

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.