## 5 Cloud-Base Big Video Data Processing  
Figure 4 shows the video data processing by the MapReduce Analyzer (MA). Firstly, video input data are divided by static offset. Our approach to divide input video data is different from others' approach. For example, [PERSON] et al. [20] divide the input video source by chunk size, however we divided the input video source by the video static Group-Of-Picture (GOP) length. When video data are stored, it is usually compressed and stored using a codec. Special care must be taken when dividing a video data compressed in GOP units using a codec. GOP consists of a key frame and a non-key frame. The key frame is a frame that is independently restored without referring to other frames and is also called an Infra Frame (I-frame). The non-key frame has only information created by the difference between the current frame and the previous I-frame. Therefore, when dividing video data without considering the GOP, the information of the I-frame, which is a key frame, is lost, and the data of other frames that refer to the key-frame cannot be read. As a result, the video data is damaged. To prevent this, the compressed file can be restored and then divided, but it is inefficient because it takes a very long time to process the encoding operation. Therefore, in this study, in order to prevent the key frame from being lost the video data was divided by the GOP unit. When dividing video data by the GOP unit, there are several other advantages, and there are other considerations, but they were not explained in this paper. The MA divides each video source into segments by GOP units and provides them to the Mapper with the key-value pairs of the path information of each divided segment and the offset. Table 1 shows the key-value pairs of our MA. Secondly, the Mapper does its given work. Thirdly, the Mapper produces an output in key-value pairs of \"video ID\" and \"analyzed data\". Fourthly, the intermediate data are sorted by \"video ID\". Fifthly and finally, the Reducer does receive the key value pair of \"video ID, list (analyzed data)\" does its given work, sorts the analyzed data by time, and writes the output to HDFS. Figure 4: The video data processing by the MapReduce Analyzer. The MA can do video processing such as object detection, object recognition, object tracking, and behavior analysis. The functional modules in the MA can be replaced so that the administrator can select the appropriate Mapper module and a Reducer module from UTOPIA's library of the video surveillance system according to smart city requirements and predefined scenarios. The video surveillance system processes big video data with UTOPIA's Cloud computing system. Figure 5 shows that when a cloud computing service is required, the USVS can request the cloud computing service through the Cloud Computing Platform (CCP) to receive the required service. The Cloud Computing Engine (CCE) of the CCP provides Cloud computing services to the USVS by managing Cloud computing resources. Figure 6 shows a brief architecture of the UTOPIA CCE. UTOPIA's Cloud supports both the domestic Cloud and the foreign Cloud and both the private Cloud and public Cloud. The domestic Cloud refers to having Cloud computing resources inside the UTOPIA infrastructure. The foreign Cloud refers to having Cloud computing resources outside the UTOPIA Infrastructure. Various types of hybrid Cloud computing are possible through the aggregation of them in UTOPIA. The UTOPIA CCE leases computing resources based on a VM (Virtual Machine) infrastructure and usually supports the master-slave deployment setting as shown in Figure 7. Users such as the system programmer specify the required computing power using the user menu provided by the UTOPIA CCP. Users can fix the requested computing power or ask an intelligent service to adjust the computing power to the required computing power automatically and dynamically after the first run.