Discussing a paper that presents a new method for managing changes in data warehouses called Temporal Dimensional Modeling. It argues that current methods, known as Slowly Changing Dimensions, have limitations that make them unsuitable for high-performance and real-time data warehouses. The paper introduces a new method that utilizes temporal dimensions which are temporally independent from fact tables. This approach improves write performance and eliminates anomalies that arise from temporal dependency. It also introduces a new concept called twine, which optimizes performance during query execution. The paper includes a comparative analysis of Temporal Dimensional Modeling and Slowly Changing Dimensions through a series of experiments demonstrating its effectiveness and advantages.

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