DEVELOPMENT OF SPACE TIME CUBE BASED DATA STRUCTURE IN MONGODB FOR URBAN DATA MANAGEMENT

• Main Author: azmy al farasyi, Ghiffari
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/42831
• Institution: Institut Teknologi Bandung

Enormous volumes and spatio-temporal attributes of urban data become challenges in urban data management. Although today there are many relational databases that have spatial extensions to handle this, they haven't been able to handle query processes on very large spatio-temporal data. This research focuses on developing a data structure that can accommodate fast query processes on spatio-temporal data using MongoDB. The data structure will be able to accommodate other data that do not have spatio-temporal attributes so the data structure is expected to store all types of urban data. The developed data structure in this research utilizing the Space Time Cube (STC) to handling urban data that has spatio-temporal attributes. In the research conducted by Chen et al, spatiotemporal data is linked to an STC where each cell in the STC represents space (x-axis and y-axis) and also represents time (z-axis). The STC's cell size is adjusted to the location where this data structure will be implemented, by considering several things such as area, congestion level, and road structure. Data structures that can accommodate urban data both those that have spatio-temporal attributes and those that do not have spatio-temporal attributes successfully build in MongoDB. Based on experiments conducted on the data structure, the data structure can support urban data query models, either single-source query, origin-destination query, or multi-source query model. In the future work, the data structure can be implemented to develop various applications that support visual analysis that can help urban planning.