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Range aggregate queries (RAQs) are an integral part of many real-worldapplications, where, often, fast and approximate answers for the queries aredesired. Recent work has studied answering RAQs using machine learning models,where a model of the data is learned to answer the queries. However, suchmodelling choices fail to utilize any query specific information. To capturesuch information, we observe that RAQs can be represented by query functions,which are functions that take a query instance (i.e., a specific RAQ) as aninput and output its corresponding answer. Using this representation, weformulate the problem of learning to approximate the query function, andpropose NeuroDB, a query specialized neural network framework, that answersRAQs efficiently. NeuroDB is query-type agnostic (i.e., it does not make anyassumption about the underlying query type) and our observation that queriescan be represented by functions is not specific to RAQs. Thus, we investigatewhether NeuroDB can be used for other query types, by applying it to distanceto nearest neighbour queries. We experimentally show that NeuroDB outperformsthe state-of-the-art for this query type, often by orders of magnitude.Moreover, the same neural network architecture as for RAQs is used, bringing tolight the possibility of using a generic framework to answer any query typeefficiently.

NeuroDB: A Neural Network Framework for Answering Range Aggregate Queries and Beyond