SDSAM: a service-oriented approach for descriptive statistical analysis of multidimensional spatio-temporal big data

With the expansion of the Internet of Things, spatio-temporal data has been widely used and generated. The rise of 'big data' in space and time has led to the flood of new applications with statistical analysis characteristics. In addition, applications based on statistical analysis of these data must deal with the large capacity, diversity and frequent changes of data, as well as the query, integration and visualisation of data. Developing such applications is essentially a challenging and time-consuming task. In order to simplify the statistical analysis of spatio-temporal data, a service-oriented method is proposed in this paper. This method defines the model of spatio-temporal data service and functional service. Define a process-based application of spatio-temporal big data statistics to invoke basic data services and functional services. This paper proposes an implementation method of spatio-temporal data service and functional service based on Hadoop environment. Taking the highway big data analysis as an example, the validity and applicability of this method are verified. The effectiveness of this method is verified by an example. The validity and applicability of the method are verified by the case study of expressway large data analysis. An example is given to verify the validity of the method.