SCOUT: Prefetching for Latent Feature Following Queries
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, pp. 1531-1542 (2012) 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...
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| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
01-08-2012
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, pp.
1531-1542 (2012) 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. |
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| DOI: | 10.48550/arxiv.1208.0276 |