GPR-Tree: a global parallel index structure for multiattribute declustering on cluster of workstations

R-tree is a very popular dynamic access structure cable of storing multidimensional and spatial data. Considering it´s merit of the efficient global balance and dynamic reorganization, we try to use R-tree to decluster the multiattribute data in database system or file system. As many previous multiattribute declustering mechanisms do not take into account the properties of the Cluster of Workstations (COW), we present the Global Parallel R-tree (GPR-Tree) under the architecture of COW. Firstly we inspect the issues in efficiency of R-tree and it´s variants, we try to enhance the R-Tree efficiency by using heuristics information in the reconstruction of R-Tree during the node splitting and the treatment of the orphan entries of the underfilled node. Then we parallelize the improved R-Tree among the components in the system. The basic thought is to alleviate the bottleneck effect of the I/O subsystem, making use of the high speed network communication and the memory. The GPR-Tree is shared among the processing units (PU) of the system. We use a mixed LRU algorithm to schedule pages in memory to maintain the nodes visited frequently in memory. A write-update-like protocol is used to keep the coherency among multiple copies maintained in the system. This mechanism is proved efficient to improve the salability and performance of the system