Experimental Analysis and Assessment of a Real-Life Cloud Mobile Big OLAP System Enhanced with Compression and Approximation Paradigms

The increasing complexity and volume of data in Cloud-based environments have posed significant challenges for Online Analytical Processing (OLAP), especially on resource-constrained mobile devices. This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, a novel technique for compressing and approximating multidimensional OLAP data cubes. IQTS efficiently handles large data cubes by flattening dimensions, employing Quad-Tree partitioning, and leveraging efficient Indexing methods. These processes significantly reduce storage requirements while ensuring high accuracy in query responses. Our experimental evaluation, utilizing real-world medical datasets, demonstrates that IQTS outperforms existing data compression techniques such as MinSkew, STHoles, and GenHist, delivering superior results in terms of space efficiency and query accuracy. The paper demonstrates the algorithm's potential for mobile Cloud environments, showing its scalability and adaptability for big data applications.