| Title | Data Flow Management and Visual Analytic for Big Data Smart City/IOT |
|---|---|
| ID_Doc | 45458 |
| Authors | Bellini, P; Bugli, F; Nesi, P; Pantaleo, G; Paolucci, M; Zaza, I |
| Title | Data Flow Management and Visual Analytic for Big Data Smart City/IOT |
| Year | 2019 |
| Published | |
| Abstract | In recent years, the number of Internet of Things and Internet of Everything (IOT/IOE) paradigms has increased significantly. The large number of devices contributed to generate a huge amount of data (Big Data) inserted in Smart City solutions, which are experiencing an explosion of complexity, also due to the increment of protocols, formats and providers. In this perspective it becomes essential to create a data indexing infrastructure that can optimize the performance of the system itself, for creating the so called data shadowing on IOT and other data on cloud. Therefore, it is fundamental to study paradigms to manage the indexing and visual analytics a great variety of data including IOT/IOE. One of the important aspects to be addressed for managing data in the smart city context are: the uniform model, the performance and scalability, response times in research, and the possibilities of performing visual analytic such as data flow analysis and drill down. All these needs imply the creation of a Smart Solution capable of managing and analysing heterogeneous kinds of data, providing a multitude of final applications based on the type of user who requires a certain service. To this end, in this paper, a unified model for IOT/IOE and data ingestion is presented. In addition, two possible architectural solutions have been implemented and compared in terms of performance, resource consumption, reliability and visual analytic tools for data flow. The solutions proposed for data indexing and shadowing have been tested in the context of Snap4City pilot Helsinki and Antwerp for smart city of EC project Select4Cities. |
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