Learning Set Cardinality in Distance Nearest Neighbours

Distance-based nearest neighbours (dNN) queries and aggregations over their answer sets are important for exploratory data analytics. We focus on the Set Cardinality Prediction (SCP) problem for the answer set of dNN queries. We contribute a novel, query-driven perspective for this problem, whereby answers to previous dNN queries are used to learn the answers to incoming dNN queries. The proposed novel machine learning (ML) model learns the dynamically changing query patterns space and thus it can focus only on the portion of the data being queried. The model enjoys several comparative advantages in prediction error and space requirements. This is in addition to being applicable in environments with sensitive data and/or environments where data accesses are too costly to execute, where the data-centric state-of-the-art is inapplicable and/or too costly. A comprehensive performance evaluation of our model is conducted, evaluating its comparative advantages versus acclaimed methods (i.e., different self-tuning histograms, sampling, multidimensional histograms, and the power-method).