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Abstract
Evaluating similarity queries over image collections effectively and
efficiently is an important but difficult issue. In many settings, a
system does not deal with individual queries in isolation,
there rather is a stream of queries. Researchers have proposed a
number of query-evaluation alternatives and generalizations, in
particular parallel methods over several components, and methods that
yield approximate results. Choosing a plan for a given query is subject
to more criteria than in conventional settings, notably result quality
next to response time and resource consumption. We have designed and
implemented a query planner that incorporates these concepts. We
describe our space of possible plans and how we search this space. The
usefulness of such a planner depends on a number of criteria, e.g.,
increase of throughput, adaptivity to different workloads, query
planning overhead, or influence of the scoring function in quantitative
terms. This article describes respective evaluations and shows that the
benefit of our particular approach is significant.
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