A Practical Yet Accurate Real-Time Statistical Analysis Library for Hydrologic Time-Series Big Data

Abstract

Using different statistical analysis methods to examine hydrologic time-series data is the basis of accurate hydrologic status analysis. With the wide application of the Internet of Things and sensor technologies, traditional statistical analysis methods are unable to meet the demand for real-time and accurate hydrologic data analysis. The existing mainstream big-data analysis platforms lack analysis methods oriented to hydrologic data. In this context, a real-time statistical analysis library based on the new generation of big data processing engine Flink, called HydroStreamingLib, was proposed and implemented. Furthermore, in order to prove the efficiency and handiness of the proposed library, a real-time statistical analysis system of hydrologic stream data was developed based on the concepts available in the proposed library. The results showed that HydroStreamingLib provides users with an efficient, real-time statistical verification method, thus extending the application capabilities of Flink Ecology in some specific fields.