Towards Querying and Visualization of Large Spatio-Temporal Databases

In any database model, data analysis can be eased 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 iteration through the huge parental data in further analysis. A subset, its specification, or the formal process for its extraction can be complex. In the database community, subsets are extracted through SQL-like queries and through visualization in the Geographic Information System (GIS) community. Both are iterative processes. An SQL query can be a composition of subqueries. Each subquery can be seen as an iterative step toward the extraction of the desired subset. For this to work, subqueries should result into relations that have the same structure as the relations in a given data model. Although it may not be immediately obvious, the visualization can be iterative too. Each community works in its own compartment. Either one uses subprocesses that are only subqueries or only visual interactions. Mixing these two subprocesses would yield a more powerful expressibility in the hands of users. Parametric Data Model is well-known for handling multidimensional parametric data, such as spatial, temporal, or spatio-temporal. In the parametric approach, the object is modeled as a single tuple, creating one-to-one correspondence between an object in the real world and a tuple in the database. The parametric approach relies on its own SQLlike, but richer, query language called ParaSQL which mimics the classical SQL. However, it is simpler and avoids self-join operations; hence, enhances performance. In the parametric approach, the attribute values are defined as a function, allowing large