Metadata is essential for understanding information stored in data warehouses. It helps increase levels of adoption and usage of data warehouse data by knowledge workers and decision makers. A metadata model is important to the implementation of a data warehouse; the lack of a metadata model can lead to quality concerns about the data warehouse. A highly successful data warehouse implementation...

Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system’s conceptual model, and familiarity with data warehouse technologies. The suitability of current data warehouse modeling methods for large-scale systems is questionable, as they require multiple manual actions to discover measur...

Data warehouse is a collection of huge database which is subject oriented, integrated, time-variant and non volatile. As it is a set of huge database, fast data access is the major performance parameter of any data warehouse. Generally the information retrieved from Data Warehouse is summarized or aggregated as it is required for some decision making process of organization. To retrieve such a ...

Although many data warehouse systems have been developed in recent years, the practice of data warehouse is still immature. Most of current systems have some limitations in terms of flexibility, efficiency and scalability. Furthermore, the contents of a data warehouse are becoming a large and messy jungle. It is difficult to maintain and analyze the data. Much research has been done in data mar...

Traditional enterprise warehouse solutions center around an analytical database system that is monolithic and inflexible: data needs to be extracted, transformed, and loaded into the rigid relational form before analysis. It takes years of sophisticated planning to provision and deploy a warehouse; adding new hardware resources to an existing warehouse is an equally lengthy and daunting task. A...

Data warehouse is considered as the core component of the modern decision support systems. Due to the major support of data warehouse in the daily transaction of an enterprise, the requirements for the design and the implementation of DW are dynamic and subjective. This dynamic nature of the data warehouse may reflect the evolution in the data warehouse. Data warehouse evolution may be focused ...

DEFINITION Extraction, Transformation, and Loading (ETL) processes are responsible for the operations taking place in the back stage of a data warehouse architecture. In a high level description of an ETL process, first, the data are extracted from the source data stores that can be On-Line Transaction Processing (OLTP) or legacy systems, files under any format, web pages, various kinds of docu...

Data warehouse is a process of evolution and is a silver bullet of any one sector either public sector or private sector. As is universal truth, after specific time period database of these sector degrade its performance due to several reason, therefore reengineering of data warehouse is needed to increases the performance of data warehouse and reduces the risk associated with the modification ...

The concept of a generalized quantiier of a given similarity type was deened in Lin66]. Our main result says that on nite structures diierent similarity types give rise to diierent classes of generalized quantiiers. More exactly, for every similarity type t there is a generalized quantiier of type t which is not deenable in the extension of rst order logic by all generalized quantiiers of type ...