  Visual exploration of large multidimensional datasets has seen tremendous
progress in recent years, allowing users to express rich data queries that
produce informative visual summaries, all in real time. Techniques based on
data cubes are some of the most promising approaches. However, these techniques
usually require a large memory footprint for large datasets. To tackle this
problem, we present NeuralCubes: neural networks that predict results for
aggregate queries, similar to data cubes. NeuralCubes learns a function that
takes as input a given query, for instance, a geographic region and temporal
interval, and outputs the result of the query. The learned function serves as a
real-time, low-memory approximator for aggregation queries. NeuralCubes models
are small enough to be sent to the client side (e.g. the web browser for a
web-based application) for evaluation, enabling data exploration of large
datasets without database/network connection. We demonstrate the effectiveness
of NeuralCubes through extensive experiments on a variety of datasets and
discuss how NeuralCubes opens up opportunities for new types of visualization
and interaction.
