Abstract. In this paper, we present a new approach to indexing multidimensional data that is particularly suitable for the efficient incremental processing of nearest neighbor quer...
For autonomous artificial decision-makers to solve realistic tasks, they need to deal with searching through large state and action spaces under time pressure. We study the probl...
We researched to try to find a way to reduce the cost of nearest neighbor searches in metric spaces. Many similarity search indexes recursively divide a region into subregions by u...
Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadth...
Andy Yoo, Edmond Chow, Keith W. Henderson, Will Mc...
We propose a partitioning scheme for similarity search indexes that is called Maximal Metric Margin Partitioning (MMMP). MMMP divides the data on the basis of its distribution pat...