At their root, agile data warehousing methods incorporate practices such as Scrum or Kanban to accelerate programming, but this strategy alone is not enough because poor software engineering practices in the phases leading up to or following application coding can fatally undermine an enterprise data warehousing (EDW) project.
The suitability of current data warehouse modeling methods for large-scale systems is questionable, as they require multiple manual actions to discover measures and relevant dimensional entities and they tend to disregard the system's dynamic aspects.
This paper presents a UML-based data warehouse design method that spans the three design phases (conceptual, logical and physical).
In their attempts to model a decision support system based on the data warehouse approach, the proposed methods have often relied on various multidimensional models to specify data mart schemas, including the star, constellation, and snowflake schema models [20, 25].
To simplify the task of constructing wrapper/monitor for the information sources in data warehouse systems, we provide a modularized design method to re-use the code.
An outline of the user requirements and a description of TCl-Db, the data warehouse built as a taxonomic infrastructure for TreeBASE, and the methods of query expansion are then given.
The review endorses the development a data warehouse for GP records in order to deliver a safe and secure method of storing and sharing patient information.
Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse.
We present an Object-process-based Data Warehovercomestheseion (ODWC) method that overcomes these limitations of existing methods by utilizing the operational system conceptual model to construct a corresponding data warehouse schema.
To the best of our knowledge, no method has incorporated both alimentation side and analysis side in the instauration of an active data warehouse.
