Data Declustering for Efficient Range and Similarity Searching

Advances in processor and network technologies have catalyzed the growth of data intensive applications such as image repositories and digital libraries. The lack of commensurate improvements in storage systems has resulted in I/O becoming a major bottleneck in modern systems. The use of parallel I/O from multiple devices is a well known technique for improving I/O performance. A key factor in exploiting parallel I/O is knowledge of the access pattern - the sets of data items that are likely to be accessed concurrently should be declustered across the disks. Range and nearest-neighbor (similarity) queries are the most important class of queries for multimedia databases. Declustering schemes tailored for improving the performance of range only or similarity only queries have been proposed in the literature. The problem of declustering for combined range and similarity queries has not been addressed in the literature. We evaluate the performance of declustering schemes for combined range..