A novel temporal and spatial panorama stream processing engine on IoT applications
Abstract Nowadays, more and more streaming data are generated with the development of Internet of Things. Although, streaming data show great application values in practical scenarios, raw streams from terminal sensors are quite massive, heterogeneous and complex, and those features make it difficult for applications to deal with them. In order to simplify streaming data processing for applications, a novel temporal and spatial panorama stream processing engine is proposed. This stream processing engine forms an effective link between bottom sensors and upper stream based applications, and provides configurable, flexible, available and usable stream services for various upper applications. With support of Internet plus and domain data, raw streaming data are formatted thorough data fusion and are represented in temporal, spatial, logic and storage views. Fusion data are encapsulated with multiple strategies and methods of clustering models on every dimension. According to configurable strategies, encapsulated data are extracted, partitioned and distributed as the form of dynamical variable gratitude data blocks into stream channels. Our engine is applied to a practical application scenario. Case study of dynamic power distribution application based on people crowd streaming data proves the customized service capabilities of our engine. And the outstanding performance of the engine is shown further by experiments evaluation in this case.