6. A new architecture for CIoT and big data

In this section, we present a new architecture for CIoT and big data. Thus, we map the system components to more concrete oriented system components. Hence, we present the new architecture for CIoT and big data.


Fig 9. New architecture for CIoT and big data.


We analyze the data processing within proposed existing technologies at the related work section. This aims to enhance the data collection processing. Thus, we must to deal with the variety of data and transmit collected data to a structured data that can be analyzed. Therefore, in the first and second layer, a tool is required to collect data from various sources using smart device features such as human sensors, user input, documents, environmental sensor, localization, and movement. This tool can extract and recognize data from unstructured data which is a big challenge for smart devices. However, it has to deal with unstructured data like image recognition, text analysis, and speech recognition. Hence, an integrated methods into the tool through algorithms can make the device thinking like a human being and recognizing what in the collected data. In the knowledge and decision making layer, the output data from the tool and other data can be extracted and loaded into a central storage such as "DL". Besides, it will be transformed into the dataset. Furthermore, an ELT is able to store it into the Data WareHouse (DWH). Data cube can send this data to a model. In addition, the data analysis makes information and analysis results available to end-user through an intelligent service. The architecture defines a solution for heterogeneous data and 3V requirements while relying on the tool and existing technologies. The tool can handle the variety of collected data from different sources. It is able to transform different types of data into one file with one format. It draws connections between different sources and provides a file that collects all the data. Moreover, The utility of Data Lake (DL) as a data store in highlights a solution for many important challenges such as the 3V requirements. Data can be ingested as they are. Hence, the user can add DL with their native format. This leads to ensuring the scalability of the architecture. Thus, DL can handle unlimited quantity of data. It can produce data sets to integrate with the traditional DWH which is well-situated to deal with the needs of automated reporting. However, DL has an analytic power such as the flexibility of their analytics requirement. Otherwise, the creativity of DWH is limited and lacks the required detail for their models. Therefore, merging both of DWH and DL improve the enterprise performance. DWH is responsible of the automated standardized analysis and the reporting capabilities while DL deals rapidly and efficiently with the complex analysis.