Independent Researcher.
Review Article
World Journal of Advanced Research and Reviews, 2019, 03(03), 062-071
Received on 12 September 2019; revised on 20 October 2019; accepted on 25 October 2019
Combining ordinary ML algorithms with the extraordinary technology of OLAP creates a novel way of improving the accuracy of predictive models. The multidimensional analysis used in OLAP systems is useful for processing large amount of data and the modernization through the ML algorithms in Decision making systems offers useful prediction technique. This study examines the implementation of OLAP with ML algorithms, including decision trees, neural networks, and regression to enhance the predictive and real-time data capability. It is extended by integrating ML features into an OLAP system and its performance is evaluated on a large-scale BI data set from a similar BI application.
The approach includes tuning OLAP system, choosing right set of ML algorithms, as well as designing an integration approach to achieve high prediction accuracy with reasonable computational cost. Findings indicate that the use of ML in conjunction with OLAP leads to better predictive performance and better scalability, including with respect to dealing with large numbers of attributes and query types. The paper also shows the business intelligence advantages of this approach, including the more precise identification of trends, risks, and opportunities. Moreover, this research highlights potential problems, including data compatibility and system performance, for future study in this area.
Machine Learning; Olap Systems; Predictive Analytics; Data Integration; Business Intelligence
Preview Article PDF
Guru Prasad Selvarajan. Integrating machine learning algorithms with OLAP systems for enhanced predictive analytics. World Journal of Advanced Research and Reviews, 2019, 3(3), 062-071. Article DOI: https://doi.org/10.30574/wjarr.2019.3.3.0064