Data warehouse is an intelligent store of data that can aggregate vast amounts of information. A metadata is critical for implementing data warehouse. Therefore, integrating data warehouse with its metadata offers a new opportunity to create a more adaptive information system. This paper proposes a metadata-oriented data warehouse architecture that consists of seven components: legacy system, extracting software, operational data store, data warehouse, data mart, application, and metadata. A taxonomy for dataflow and metaflow is proposed for better understanding of the architecture. In addition, a metadata schema is built within the framework of the seven components. The architecture with its metadata component is applied to a real-life data warehouse for a large medical center in order to illustrate its practical usefulness.

Hypermedia extends the hypertext paradigm into multimedia. Recently, many enterprises have attempted to incorporate hypermedia functionality into their diverse range of business applications such as decision support systems, document management, software management, and team collaboration. This paper presents a methodology for designing hypermedia applications based on users’ views. In designing hypermedia applications, view support is highly desirable. The methodology consists of five phases: requirement analysis, data modeling, view design, navigational design and mapping. Views are generated from the entityrelationship diagram, and used for subsequent navigational and mapping design. This methodology is especially effective for integrating enterprise databases with distributed hypermedia systems on the InternetRequest access from your librarian to read this article's full text.

This paper compares conceptual data models including extended entity relationship (EER), semantic object model (SOM), object role modeling (ORM), and object modeling technique (OMT) in terms of model correctness, modeling time, and perceived ease-of-use. For an empirical study, 28 graduates and 72 undergraduates were selected and then divided into four equally sized groups. Each group was trained with one data modeling technique. Two cases were used; one was prepared in natural language and the other in enterprise form. The study results show some differences among the four conceptual data modeling techniques. These positive findings may help modelers better understand modeling techniques.Request access from your librarian to read this article's full text.