Advances in hardware-related technologies promise to enable new data management applications that monitor continuous processes. In these applications, enormous amounts of state samples are obtained via sensors and are streamed to a database. Further, updates are very frequent and may exhibit locality. While the R-tree is the index of choice for multi-dimensional data with low dimensionality, and is thus relevant to these applications, R-tree updates are also relatively inefficient. We present a generalized bottom-up update strategy for R-trees that generalizes existing update techniques and aims to improve update performance. It has different levels of reorganizationranging from global to localduring updates, avoiding expensive top-down updates. A compact main-memory summary structure that allows direct access to the R-tree index nodes is used together with efficient bottom-up algorithms. Empirical studies show that the bottom-up strategy outperforms the traditional top-down technique, leads to indices with better query performance, achieves higher throughput, and is scalable.
|Title of host publication||Proceedings of the Twentynineth International Conference on Very Large Data Bases, Berlin, Germany, September 911|
|Publication status||Published - 2003|
|Event||Supporting Frequent Updates in R-Trees: A Bottom-Up Approach - Busan, Korea, Republic of|
Duration: 17 Nov 2003 → 17 Nov 2003
|Conference||Supporting Frequent Updates in R-Trees: A Bottom-Up Approach|
|Country||Korea, Republic of|
|Period||17/11/2003 → 17/11/2003|