Evaluating statistical tests on OLAP cubes to compare degree of disease

Online Analytical Processing for Business Intelligence in Big Data

Online analytical processing (OLAP) approach is widely used in business intelligence to cater the multidimensional queries for decades. In this era of cutting-edge technology and the internet, data generation rates have been rising exponentially. Internet of things sensors and social media platforms are some of the major contributors, leading toward the absolute data boom. Storage and speed are the crucial parameters and undoubtedly the burning issues in efficient data handling. The key idea here is to address these two challenges of big data computing in OLAP. In this article, the authors have proposed and implemented OLAP on Hadoop by Indexing (OOHI). OOHI offers a simplified multidimensional model that stores dimensions in the schema server and measures on the Hadoop cluster. Overall setup is divided into various modules, namely: data storage module (DSM), dimension encoding module (DEM), cube segmentation module, segment selection module (SSM), and block selection and process (BSAP) module. Serialization and deserialization concept applied by DSM for storage and retrieval of the data for efficient space utilization. Integer encoding adopted by DEM in dimension hierarchy is selected to escape sparsity problem in multidimensional big data. To reduce search space by chunks of the cube from the queried chunks, SSM plays an important role. Map reduce-based indexing approach and series of seek operations of BSAP module were integrated to achieve parallelism and fault tolerance. Real-time oceanography data and supermarket data sets are applied to demonstrate that OOHI model is data independent. Various test cases are designed to cover the scope of each dimension and volume of data set. Comparative results and performance analytics portray that OOHI outperforms in data storage, dice, slice, and roll-up operations compared with Hadoop based OLAP.