Big data-based query optimization for business intelligence

Business intelligence (BI) is a compilation of business decision-support technologies that help skilled professionals such as supervisors, managers, and experts make real-time decisions. The number of products and services that the industry has supplied and the use of these technologies have witnessed a significant decreasing the acquisition and storage of vast quantities of data from various sources, which has driven this increase. This paper proposes big data querying and correlative analytical techniques (BDQCA) to evaluate complex and interactive queries where typical query processing systems cannot handle big data queries. The MapReduce paradigm is built to analyze and search web documents from query logs in business analysis. The online Analytic processing(OLAP) framework is introduced to promote operations for multiple database perspectives, such as filtering, aggregation, roll-up, and drill-down. Therefore, this research identifies different big data strategies for a business and provides several corporate business continuity factors that influence strategic decisions. The simulation evaluations are performed based on the robustness of the proposed framework, the performance ratio of 97.1%, and the efficiency ratio of 95.5%.