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Finding nearest neighbors has become an important operation on databases, with applications to text search, multimedia indexing, and many other areas. One popular algorithm for similarity search, especially for high dimensional data (where spatial indexes like kd-trees do not perform well) is Locality Sensitive Hashing (LSH), an approximation algorithm for(More)
MapD, or "Massively Parallel Database", is a big data analytics platform that can query and visualize big data up to 100x faster than other systems. It leverages the massive parallelism of commodity GPUs to execute SQL queries over multi-billion row datasets with millisecond response times, and optionally render the results using the GPU's native graphics(More)
Dubey. 2013. Streaming similarity search over one billion tweets using parallel locality-sensitive hashing. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. ABSTRACT Finding nearest neighbors has become an important operation on databases, with applications to text search, multimedia(More)
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