Virtual DataStack for Application Domains: Concepts, Challenges and Generation Techniques

Dataset plays a major role in evaluating Machine Learning (ML), Artificial Intelligence (AI), Business Intelligence (BI) and enterprise software. To prove the efficacy of any algorithm / software and also its limitation with respect to the size of the input data, it has to be exercised on a large dataset. Creating such a large dataset is a challenging task especially with near real time data. In this paper, a layered architecture to construct Virtual DataStack (VDS) is proposed. The VDS contains near real time synthetic virtual datasets (specific to a particular domain) along with other components that comprises the generation of datasets. Virtual DataStack for CampusStack (academic campus management) domain has been generated and results prove that Query Inclusive Computational Logic reduces lines of code as well as the time taken significantly. This paper focuses mainly on efficient generation of near real time data for enterprise domains with a specific attention to Education Domain and CampusStack.