Abstract
A real-time data warehouse (RTDW) allows decision makers to analyse fresh data as fast as possible in order to support real-time decision processes. In this paper, we focus on optimisation techniques to speed up query processing; in particular, we propose a dynamic selection of materialised views algorithm (DynaSeV) which selects views from results of incoming queries. Secondly, we suggest a new update policy to dynamically maintain materialised views. In addition, we propose a novel data partitioning approach for RTDW, called 2LPA-RTDW (Two-Level data Partitioning Approach for RTDW) by allowing unbalance of data amount in each partition. Then, we present our architecture called DETL-(m, k)-firm-RTDW architecture (decentralised extract-transform-load approach based on (m, k)-Firm constraints for RTDW) which deals with diversity and disparities in data source systems to reduce the time for ETL. Finally, we evaluate our contributions using the TPC-DS (TPC, 2014) benchmark; the preliminary results are quite promising.
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