SCOUT: Prefetching for Latent Feature Following Queries

TL;DR

SCOUT is a structure-aware prefetching method that significantly accelerates interactive spatial queries in scientific data analysis by accurately predicting user-followed structures, achieving up to 15x speedup.

Contribution

The paper introduces SCOUT, a novel prefetching approach that uses an approximate graph model to improve accuracy in following spatial structures during queries.

Findings

Prefetching accuracy ranges from 71% to 92%.

Achieves 4x to 15x speedup in query response times.

Effective across neuroscience, medicine, and biology datasets.

Abstract

Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack the tools to efficiently work with data of this size. One problem many scientists share is the analysis of the massive spatial models they build. For several types of analysis they need to interactively follow the structures in the spatial model, e.g., the arterial tree, neuron fibers, etc., and issue range queries along the way. Each query takes long to execute, and the total time for executing a sequence of queries significantly delays data analysis. Prefetching the spatial data reduces the response time considerably, but known approaches do not prefetch with high accuracy. We develop SCOUT, a structure-aware method for prefetching data along interactive spatial query sequences. SCOUT uses an approximate graph model of the structures involved in past queries and attempts to identify what particular structure the user follows. Our experiments with neuroscience data show that SCOUT prefetches with an accuracy from 71% to 92%, which translates to a speedup of 4x-15x. SCOUT also improves the prefetching accuracy on datasets from other scientific domains, such as medicine and biology.