The DC-Tree: A Fully Dynamic Index Structure for Data Warehouses

Many companies have recognized the strategic importance of the knowledge hidden in their large databases and have built data warehouses. Typically, updates are collected and applied to the data warehouse periodically in a batch mode, e.g., over night. Then, all derived information such as index structures has to be updated as well. The standard approach of bulk incremental updates to data warehouses has some drawbacks.First, the average runtime for a single update is small but the total runtime for the whole batch of updates may become rather large. Second, the contents of the data warehouse is not always up to date. In this paper, we introduced the DC-tree, a fully dynamic index structure for data warehouses modeled as a data cube. This new index structure is designed for applications where the above drawbacks of the bulk update approach are critical. The DC-tree is a hierarchical index structure similar to the X-tree exploiting the concept hierarchies typically defined for the dimensions of a data cube. The DC-tree uses minimum describing sets and the partial ordering of the attribute values induced by the concept hierarchies instead of minimum bounding rectangles and an artificial total ordering. Furthermore, for each minimum describing set in the directory the values of the measure attributes are materialized. We conducted an extensive experimental performance evaluation using the TPC-D benchmark data. Our results demonstrate that the DC-tree yields a significant speed-up compared to the X-tree and the sequential search when processing general range queries on a data cube.