A Novel approach to Data Visualization by supporting Ad-hoc Query and Predictive analysis : (An Intelligent Data Analyzer and visualizer)

Abstract

Business Intelligence tools help to present a snapshot of the company by using graphical tools like pie charts, bar graphs, dashboards, etc. which facilitates easy understanding and decision-making. However, measures can be adopted to make BI tools more user-friendly. Our paper is an improvement over the existing BI tools as it supports predictive analytics along with the existing functionalities offered by any BI Tool. Our proposal also enables the user to ask queries in natural language format. This application analyses the query structure and categorizes it as a classification, regression, clustering, etc. problem. Once the query is categorized, it can then be processed by applying all different algorithms which are supported by Apache Spark’s machine learning library MLlib within each category. These algorithms are compared based on various evaluation metrics like accuracy, precision etc. and the most suitable algorithm is used to form the final predictive model. A labelled dataset ensures that our predictive analysis model needs to focus on Supervised learning algorithms only. The results computed are then represented in a graphical format for ease of comprehension of the management. The proposed solution exploits Apache Spark’s processing power, speed, its ability to handle huge datasets and its Machine learning support called Apache Spark MLlib. For implementation of the proposed solution we have used a MongoDB database of a windmill electricity generation plant. This proposal offers added functionalities to BI tools and improves user experience to accommodate the growing needs of the industry.