| Summary: | 博士 === 國立中央大學 === 企業管理研究所 === 95 === The scale of Information Systems applications is expanding rapidly as the business environment becomes increasingly complex; for example, apart from supporting daily operations, systems are becoming more specialized to support management in decision-making. It has been found that decision support systems (DSS) are critical to helping organizations gain a competitive advantage and in helping managers make decisions. Excellent decision support systems have been constructed to store, integrate, and analyze data, and to provide reporting functions. As a result, business intelligence systems and related application tools are emerging.
Many IT practitioners and researchers advocate that, to achieve maximum efficiency, data warehouse models should incorporate the sources of the data. As the source data is probably derived from systems designed with ER diagrams, a great deal of research has been devoted to the design of methodologies for building multidimensional models based on source ER diagrams. However, to the best of our knowledge, no algorithm has been proposed that can systematically translate an entire ER diagram into a multidimensional model with hierarchical snowflake structures. To fill this research gap, in the first part of this study, we propose an algorithm that achieves the above goal because it incorporates two features, namely, grain preservation and the minimal distance from each dimension table to the fact table. The grain preservation feature guarantees that the translated multidimensional model will maintain cohesive granularity among the entities. Meanwhile, the minimal distance feature guarantees that if an entity can be connected to the fact table in the multidimensional model by more than one path, the path with the smallest number of hops will always be chosen. The first feature is derived by (1) translating ambiguous relationships between entities into weighting factors stored in bridge tables, and (2) enhancing fact tables with unique primary keys. The second feature results from including a revised shortest path algorithm in the translating algorithm, with the distance being calculated as the number of relationships required between entities. A prototype system based on the methodology is also developed, and snapshots of the screens used for the system''s execution are presented.
In addition, data warehouses contain vast amounts of data with unique characteristics; hence, they are different from other information systems used in business enterprises. Several IT studies have investigated the critical success factors of data warehouses, including the availability of high quality information that is well organized, presented in a timely manner, and easily understood. A data warehouse has to be based on a core competence so that it can provide vital business intelligence to assist top managers in the decision-making process. Moreover, it should incorporate external and internal knowledge acquired over time and adapt such knowledge to current corporate conditions to support top management. Meanwhile, some studies have observed that there is low utilization of data warehouses in most organizations, and the amount of money that must be invested is much higher than for other information systems. Both low utilization and high investment costs can prevent an organization building a data warehouse system.
Although several studies have addressed the efficiency improvements derived from business intelligence systems, most of the studies are technique oriented. They focus on the data models, query expressions, client tools, or materialized views for efficient query processing. None of them consider users’ characteristics or the data’s features to increase user interest and facilitate use of the systems. Therefore, in the second part of this study, we apply data mining techniques to business intelligence query logs to develop a recommendation mechanism. Our objective is to help users obtain more information by increasing their usage of business intelligence systems.
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