# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
###### Abstract
IoT location-based systems have advantages in locating and monitoring equipment or people in real time, analyzing data, and supporting decision-making. As the number of sensors is increased, larger amounts of data can be collected in short durations of time and stored in databases. However, this may cause difficulties in data retrieval and analysis. Because large data may lead to high computation and throughput, even simple statistical analysis may take a long time. In particular, for road pavement construction sites, increases in analysis time due to the diversity of mobile systems and multiple sites and devices may lower the applicability of IoT systems. Because pavement quality data is a typical big data due to the large amount of sensor data and very long construction period, new components of data management is required for a substitution for the relational database management system (RDBMS). Therefore, this paper proposes an advanced data collection and analysis system based on big data/cloud to facilitate the use of IoT location-based smart platforms at road pavement construction sites. To enable advanced data collection and analysis, a big data platform was designed based on distributed and parallel processing for the RDBMS-based infrastructures used in conventional IoT-based systems. For this platform, a structure that can enable real-time high-speed data collection and analysis was established based on the non-structured query language (NoSQL). Subsequently, the big data system, which was designed to be able to perform real-time monitoring, was evaluated using data analysis methods suitable for road pavement construction environments. The proposed system provided improvements in data collection and analysis speed performance, thus solving the problem of insufficient data processing capacity typically encountered with conventional location-based systems and enabling analysis results to be derived in real time through advanced search and analysis. The performance of the proposed system was experimentally verified based on comparisons with a data collection and analysis system based on conventional RDBMS. The experimental results showed that the overall performance of the proposed system based on NoSQL was approximately 52 times (create) and 4.7 times (delete) higher, and that its data collection (create) performance was particularly high. Therefore, it can be inferred that the proposed system, with its data monitoring and analysis system structure, is suitable for use in IoT road pavement quality management platforms that collect data on a large scale. In particular, the contributions of this research study include the following:  
* Proposal of a smart system structure for road pavement quality improvement;
* Optimized data structure design for data pre-processing technology and road pavement process management that can determine road pavement status;
* Proposal of a framework capable of real-time process management through research and development of high-speed data processing techniques and protocols for IoT location-based road pavement quality big data;
* System structure capable of network traffic management as a component of smart city platform;
* Server scale-out technique that can be configured adaptively according to data scale. The remainder of this paper is organized as follows: Section 2 provides details on related works in the literature. Section 3 describes the proposed IoT road pavement quality management system based on NoSQL. Section 4 reports on the experiments conducted to evaluate the performance of the proposed system. Finally, Section 5 presents the conclusions of the study.