Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations

In this paper, we introduce the partial vector approximation file, an extension of the well known vector approximation file that is constructed to efficiently answer partial similarity queries in any possible subspace which is not known beforehand. The idea of the partial VA-file is to divide the VA-file into a separate file for each dimension and only load the dimensions that are necessary to answer the query. Thus, the partial VA-file is constructed to improve the query performance for systems that have to cope with a wide variety of previously unknown query subspaces. We propose novel algorithms for partial kNN and epsiv-range queries based on the new partial VA-file. In our experiments, we demonstrate that our proposed partial VA-file with the novel algorithms improves the average query performance in comparison to the original VA-file when answering partial similarity queries