A Data Warehouse Approach for Business Intelligence

Abstract

In a cloud based data warehouse (DW), business users can access and query data from multiple sources and geographically distributed places. Business analysts and decision makers are counting on DWs especially for data analysis and reporting. Temporal and spatial data are two factors that affect seriously decision-making and marketing strategies and many applications require modelling and special treatment of these kinds of data since they cannot be treated efficiently within a conventional multidimensional database. One main application domain of spatiotemporal data warehousing is telecommunication industry, which is rapidly dominated by massive volume of data. In this paper, a DW schema modelling approach is proposed which integrate in a unified manner temporal and spatial data in a general data warehousing framework. Temporal and spatial data integration becomes more important as the volume and sharing of data grows. The aim of this research work is to facilitate the understanding, querying and management of spatiotemporal data for on-line analytical processing (OLAP). The proposed new spatiotemporal DW schema extends OLAP queries for supporting spatial and temporal queries. A case study is developed and implemented for the telecommunication industry.