Provided by: Springer Healthcare
Topic: Big Data
Date Added: Jul 2012
Agile methods have been increasingly adopted to make data warehouse design faster and nimbler. They divide a data warehouse project into sprints (iterations), and include a sprint planning phase that is critical to ensure the project success. Several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the elemental functionalities included in each sprint, which makes the planning problem difficult. In this paper, the authors formalize the planning problem and propose an optimization model that, given the estimates made by the project team and a set of development constraints, produces an optimal sprint plan that maximizes the business value perceived by users.