Bit-sliced signature files for very large text databases on a parallel machine architecture
EDBT '94 Proceedings of the 4th international conference on extending database technology: Advances in database technology
Declustering of key-based partitioned signature files
ACM Transactions on Database Systems (TODS)
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Similarity query processing using disk arrays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Architecture of a networked image search and retrieval system
Proceedings of the eighth international conference on Information and knowledge management
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Trading Quality for Time with Nearest Neighbor Search
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A service-oriented grid infrastructure for multimedia management and search
DELOS'04 Proceedings of the 6th Thematic conference on Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures
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In digital libraries, nearest-neighbor search (NN-search) plays a key role for content-based retrieval over multimedia objects. However, performance of existing NN-search techniques is not satisfactory with large collections and with high-dimensional representations of the objects. To obtain response times that are interactive, we pursue the following approach: it uses a linear algorithm that works with approximations of the vectors and parallelizes it. In more detail, we parallelize NN-search based on the VA-File in a Network of Workstations (NOW). This approach reduces search time to a reasonable level for large collections. The best speedup we have observed is by almost 30 for a NOW with only three components with 900 MB of feature data. But this requires a number of design decisions, in particular when taking load dynamism and heterogeneity of components into account. Our contribution is to address these design issues.