# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
## 3 IoT Road Pavement Big Data Collection and Analysis System Based on NoSQL  
### Structure of Proposed System  
The road pavement quality management and analysis proposed in this study consists of a road-pavement big-data system architecture for storing, processing, and analyzing location-based data and sensing data collected from IoT sensors. The overall architecture of the proposed system is illustrated in Figure 4. It is configured in accordance with basic big data layers consisting of data collection, processing, storage, analysis, and visualization structures. The collection model was designed and constructed using the representational state transfer (REST) API-based Python Web Framework [21] and web application [15].  
Figure 3: SQL vs. NoSQL [19].  
When IoT devices send data via REST API, the collection model simply processes and stores them in the NoSQL database [22; 23]. The protocol is simple, and storage processing is simplified by the use of the JSON format for the transmission data. The storage model stores data through distributed processing using the NoSQL driver interface. The data are stored in document format in distributed data nodes, and metadata for indexing are generated. The search and analysis module finds documents based on required keywords for post-indexing; this module can search at high speeds and performs statistical operations by applying distributed parallel processing in the form of MapReduce. When a web server and a web application are included in the model view template (MVT) structure, the web application also collects, processes, analyzes, and visualizes data [24].  
The proposed system employs ElasticSearch [25] for data storage/management and high-speed search/analysis. Furthermore, the system architecture was designed such that high-speed data search and real-time data monitoring and analysis can be further facilitated via expansions in the data warehouse with MongoDB, a big-data-based NoSQL database structure. Because the proposed system can collect and process various heterogeneous data in real time from multiple road pavement sites, it uses a NoSQL-based data warehouse, which is not dependent on data format and is easily expandable. In addition, real-time analysis and monitoring were enabled with the use of high-speed search and data-processing technologies. Although this is also possible in conventional RDBMS-based data platforms, NoSQL, as a document-based database, is advantageous for data collection and processing in environments in which heterogeneous data and unstructured data sent from various heterogeneous devices coexist. MongoDB [26] is suitable for this purpose because it is easy to implement in the form of REST API to interoperate with an open-source-based big data platform such as Hadoop and ElasticSearch, and also to interoperate with the open-source visualization platform Grafana. The early system ElasticSearch can be modularized using analysis/search engines, and a Hadoop-MongoDB system can be constructed in a form most suitable for road pavement quality management.  
Figure 4: Proposed system.  
The system proposed in this paper is composed only of elements required in a commercial cloud environment in accordance with the definition and design of the functions and elements of cloud services. Hence, its structure is expandable to a stack environment, such as Elastic, Hadoop, or MongoDB, and is directly applicable to various platforms.