An innovative lambda-architecture-based data warehouse maintenance framework for effective and efficient near-real-time OLAP over big data

In order to speed-up query processing in the context of Data Warehouse Systems, auxiliary summaries, such as materialized views and calculated attributes, are built on top of the data warehouse relations. As changes are made to the data warehouse through maintenance transactions, summary data become stale, unless the refresh of summary data is characterized by an expensive cost. The challenge gets even worst when near real-time environments are considered, even with respect to emerging Big Data features. In this paper, inspired by the well-known Lambda architecture, we introduce a novel approach for effectively and efficiently supporting data warehouse maintenance processes in the context of near real-time OLAP scenarios, making use of so-called big summary data, and we assess it via an empirical study that stresses the complexity of such OLAP scenarios via using the popular TPC-H benchmark.