Subset, Subquery and Queryable-Visualization in Parametric Big Data Model

In a big data model (or database as interchangeably called), the data analysis can be eased out by extracting a smaller set of the data of interest, called subset, from the mammoth original dataset. Thus, a subset helps enhance the performance of a system by avoiding the unwanted iterations through the huge parent data in further analysis. In this study, the data model of interest is the Parametric Big Data Model (ParaDB), which is well-known for handling multidimensional big data. Unlike the other classical data models, where subset provides additional strength to the system and the ParaDB completely lacks this potential functionality and consequently could not become equally efficient and effective comparatively. Therefore, in this research, we implement the subset capability in ParaDB to further strengthen its robustness and to ensure the relational outcome instead of streamed-out plain text. Furthermore, to perform the preliminary investigation, the exploratory visual analysis is an important aspect in any, especially spatio-temporal, big data model. Unfortunately, the ParaDB does not offer any visualization support earlier in any format. Therefore, some comprehensive steps are taken to implement the visualization functionality in ParaDB in a way that is conducive to its structures. Additionally, the GIS-visual richness is integrated and implemented to further strengthen the visual maturity of ParaDB, where it offers the queryable-visualization