Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
On two-dimensional indexability and optimal range search indexing
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Parallel Similarity Search in High Dimensional Metric Space Using M-Tree
IWCC '01 Proceedings of the NATO Advanced Research Workshop on Advanced Environments, Tools, and Applications for Cluster Computing-Revised Papers
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Processing M-trees with Parallel Resources
RIDE '98 Proceedings of the Workshop on Research Issues in Database Engineering
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
On Integrating Peptide Sequence Analysis and Relational Distance-Based Indexing
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Approximate Similarity Search in Genomic Sequence Databases Using Landmark-Guided Embedding
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Similarity Searching: Towards Bulk-Loading Peer-to-Peer Networks
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
An Empirical Evaluation of a Distributed Clustering-Based Index for Metric Space Databases
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
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We develop and evaluate a version of the excluded middle vantage point forest in support of range searches and load balancing for parallel queries. The algorithm is evaluated using a benchmark suite that includes real-world biological sequence workloads. Favorable results are demonstrated when comparing to the Multiple Vantage Point Tree and Spatial Approximation Tree algorithms with respect to sequential measures. We also demonstrate that the performance of this approach scales linearly up to at least 128 cores and outperforms a naive distributed multiple vantage point forest approach when run in parallel.