IEEE Spectrum
Disk allocation for Cartesian product files on multiple-disk systems
ACM Transactions on Database Systems (TODS)
(Almost) optimal parallel block access to range queries
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Titan: A High-Performance Remote Sensing Database
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Cyclic Allocation of Two-Dimensional Data
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Approximation of Multi-color Discrepancy
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
Multidimensional Declustering Schemes Using Golden Ratio and Kronecker Sequences
IEEE Transactions on Knowledge and Data Engineering
Asymptotically optimal declustering schemes for 2-dim range queries
Theoretical Computer Science - Database theory
Combinatorics, Probability and Computing
Optimal Parallel Block Access for Range Queries
ICPADS '04 Proceedings of the Parallel and Distributed Systems, Tenth International Conference
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
| Hi-index | 5.23 |
The declustering problem is to allocate given data on parallel working storage devices in such a manner that typical requests find their data evenly distributed on the devices. Using deep results from discrepancy theory, we improve previous work of several authors concerning range queries to higher-dimensional data. We give a declustering scheme with an additive error of Od (logd-1M) independent of the data size, where d is the dimension, M the number of storage devices and d - 1 does not exceed the smallest prime power in the canonical decomposition of M into prime powers. In particular, our schemes work for arbitrary-M in dimensions two and three. For general d, they work for all M≥d - 1 that are powers of two. Concerning lower bounds, we show that a recent proof of a Ωd (log(d-1)/2M) bound contains an error. We close the gap in the proof and thus establish the bound.