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.

Focusing on the main research context represented by the issue of supporting big data analytics over big graph data, this paper introduces and experimentally assesses the framework MALAGA (MultidimensionAL Big DAta Analytics over Massive Graph DAta). MALAGA incorporates several innovations, including OLAP analysis of big graph data, columnar-OLAP methodologies, and Apache Hive extensions. A comprehensive experimental assessment and analysis of the framework’s performance is finally presented and discussed, by significantly integrating the conceptual contributions of our research.

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and “philosophy”. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm.