Expanding ParaSQL for spatio-temporal (big) data

Abstract

Today, most real-world applications are dealing with some form of dimensional data. In recent years, the large, heterogeneous, and multidimensional data have gained significant attention. The complex multidimensional data are being generated at a very rapid pace through various disparate potential resources and sensors, scientific instruments, and internet, especially the social media, are just to name a few. Though, the volume of the data is expanding with a considerable velocity, the data management techniques are not advancing at the same pace, resulting in the scarcity of suitably efficient data processing systems. This unexpected gap of advancement has raised serious concerns in the data community. Presently, in data science, one of the fast-growing needs is to advance the query processing system to efficiently deal with the increasingly complex and sizable data. This research work also aims to address such challenges and attempts to expand the bandwidth of the querying system of the Parametric Data Model. It is an efficient dimensional data model, which comes equipped with its own SQL-like query language, known as Parametric Structured Query Language (ParaSQL).