Real-time spatial data conversion over cloud computing / Nor Hasimah Che Mat

Geographical Information System (GIS) technology provides location-based services by manipulating geometry information to help users to navigate and spot a specific location that they are interested with. In the aspect of infrastructure facilities, GIS is also important to determine the exact location of any event related to road, such as road accident locations, census stations located along Federal and State roads in Malaysia in order to collect data of traffic volume, traffic growth and highway capacity for the purpose of monitoring and maintenance. The demand of road assets location also keeps on growing exponentially from time to time since the information is closed related to other facilities such as transportation, traffic, drainage and more others. However the rapid growth of massive spatial data requires real-time data processing to achieve rapid response mechanism of the system. On top of that, spatial data comes from multiple sources and in various formats also posed a great challenge to the developer to convert the data into standard format so that it can be shared with other parties for mutual benefits. Therefore, this study is to test the performance of real-time spatial data conversion of road data compared to the traditional method, batch conversion. By having good scalability, high reliability and high availability, cloud computing can provide powerful technical support in order to promote the capability of spatial information service. Meanwhile the powerful computing capacity provided by cloud computing services also can help to meet the real-time processing of a large amount of road spatial data. Thus, the purpose of this study is to provide an organised spatial data of road event and a better data sharing between agencies that responsible on the road assets management. Based on the study that had been carried out, it proved the spatial data conversion in real-time processing was more efficient than batch processing in term of process complexity and time cycle. Thus with these findings, it can be concluded the spatial data sharing are more efficient by implementing real-time spatial data conversion over cloud computing rather than batch spatial data conversion.