: Data warehouse technology transforms the operational data store to general and compositive information. It also provides effective way for analysis and statistic to the mass data...
Data Warehouses (DWs) use an omnipresent time dimension for keeping track of changes in measure values. However, this dimension cannot be used to model changes in other dimensions...
This paper introduces a Conceptual Data Model for Data Warehouse including multidimensional aggregation. It is based on Entity-Relationships data model. The conceptual data model ...
Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehous...
Current data warehouse and OLAP technologies can be applied to analyze the structured data that companies store in their databases. The circumstances that describe the context ass...
Different modeling approaches have been proposed to overcome every design pitfall of the development of the different parts of a data warehouse (DW) system. However, they are all ...
Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus mak...
When a data warehouse is loaded at night and queried during the day, there is no requirement for concurrent update and querying. However there are a number of situations where con...
The topic of data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a sing...
Healthcare organizations practicing evidence-based medicine strive to unite their data resources in order to achieve a wider knowledge base for sophisticated research and matured d...