A Data Warehouse stores integrated information as materialized views over data from one or more remote sources. These materialized views must be maintained in response to actual relation updates in the remote sources. The data warehouse view maintenance techniques are classified into four major categories self maintainable recomputation, not self maintainable recomputation, self maintainable in...

Data warehouse systems offer novel techniques and services for data-rich applications both from a data modeling and a data processing point of view. In this paper, we investigate how well state-of-the-art concepts fulfill the requirements of real-world data warehouse applications. Referring to a concrete example from the market research area, key problems with current approaches are identified ...

Due to shifting of healthcare data in electronic form, huge volumes of data have been accumulated in organizations related to medical claim processing domain. This paper presents design of a data warehouse implemented as integral part of a rule based expert system. The system is being used for scrubbing of medical claims’ data. Approximate star schema has been used for designing the proposed da...

A data warehouse is a conceptual repository that collects information from one or more data sources and provides a schema that allows this repository to be queried by users of the warehouse. The dii-culties in designing a schema for a data warehouse whose member databases are multimedia databases are mainly caused by the highly diverse nature of multimedia data, a very wide range of multimedia ...

The World Wide Web contains a huge and increasing volume of information. The web warehouse is an efficient and effective means to facilitate utilization of information on the Web, not only to individual users but also to business organizations, especially for decision-making purposes. On the other hand, XML has recently become the new standard for representation and exchange of data on the Web....

This paper describes a decision support system (DSS) for use in allocating scarce resources associated with data quality efforts in the construction of a data warehouse. The DSS is populated with metadata from a data warehouse project, including tags that identify the quality at intersections of data field, data use, and data dimension. The resulting DSS can then be queried by novices on a data...

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...

A data warehouse is built by collecting data from external sources. Several changes on contents and structures can usually happen on these sources. Therefore, these changes have to be reflected in the data warehouse using schema updating or versioning. However a data warehouse has also to evolve according to new users’ analysis needs. In this case, the evolution is rather driven by knowledge th...

Integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration and...

Integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration and...