Research Article
SMART: A Service-Oriented Statistical Analysis Framework on Spatio-Temporal Big Data (Short Paper)
@INPROCEEDINGS{10.1007/978-3-030-30146-0_7, author={Jie Zhou and Weilong Ding and Zhuofeng Zhao and Han Li}, title={SMART: A Service-Oriented Statistical Analysis Framework on Spatio-Temporal Big Data (Short Paper)}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={8}, keywords={Spatio-temporal data Service composition Configurable}, doi={10.1007/978-3-030-30146-0_7} }- Jie Zhou
Weilong Ding
Zhuofeng Zhao
Han Li
Year: 2019
SMART: A Service-Oriented Statistical Analysis Framework on Spatio-Temporal Big Data (Short Paper)
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-30146-0_7
Abstract
Spatio-temporal data is one of the most important assets in the context of smart cities. Spatio-temporal big data comes from a variety of sensor devices, implies the state of urban operation, insight into the development trend. Due to the multidimensional characteristics and diverse analysis needs of spatial-temporal data, data analysis based on spatial-temporal data must take into account the large capacity, diversity and frequent changes of data. This makes spatial and temporal data analysis more difficult. In order to simplify the analysis of spatio-temporal data, a service-oriented intelligent framework is proposed. Firstly, the concept of spatio-temporal data service is introduced into the framework, and several common spatio-temporal data service models are defined. Then, a configurable scripting language was proposed to define the analytic application. We also developed a prototype tool to implement spatio-temporal data services on Hadoop. In order to prove the applicability of our method, we demonstrate the effectiveness of our work through a practical application-based study.
