  Storing tabular data in a way that balances storage and query efficiencies is
a long standing research question in the database community. While there are
several lossless compression techniques in the literature, in this work we
argue and show that a novel Deep Learned Data Mapping (or DeepMapping)
abstraction, which relies on the impressive memorization capabilities of deep
neural networks, can provide better storage cost, better latency, and better
run-time memory footprint, all at the same time. Our proposed DeepMapping
abstraction transforms a data set into multiple key-value mappings and
constructs a multi-tasking neural network model that outputs the corresponding
values for a given input key. In order to deal with the memorization errors,
DeepMapping couples the learned neural network with a light-weight auxiliary
data structure capable of correcting errors. The auxiliary structure further
enables DeepMapping to efficiently deal with insertions, deletions, and
updates, without having to re-train the mapping. Since the shape of the network
has a significant impact on the overall size of the DeepMapping structure, we
further propose a multi-task hybrid architecture search strategy to identify
DeepMapping architectures that strike a desirable balance among memorization
capacity, size, and efficiency. Extensive experiments with synthetic and
benchmark datasets, including TPC-H and TPC-DS, demonstrated that the proposed
DeepMapping approach can significantly reduce the latency of the key-based
queries, while simultaneously improving both offline and run-time storage
requirements against several cutting-edge competitors.
