Advanced Big Data Approaches for Optimizing Business Intelligence Queries

Advanced big data approaches were analyzed to optimize business intelligence queries for real-time professional decision-making by managers or specialists. Indeed, with an increase in the volume of data from diverse sources, BI technologies attempt to facilitate gathering and storing the data to improve decision support. This paper puts forward a Big Data Querying and Correlative Analysis (BDQCA) approach that would deal with various complex, dynamic queries that could not be achieved using traditional systems. By resorting to the MapReduce paradigm, the approach permits the analysis of Web content based on query logs while enhancing various database perspectives by resorting to the OLAP framework and including filtering, aggregation, roll-up, and drill-down features. This study proposes defining strategic big data methodologies within an enterprise and identifies all factors influencing corporate continuity. Simulation evaluations have been performed; the obtained performance ratio using the framework was 97.1%, and the efficiency was 95.5%.