  Materialized views (MVs), stored pre-computed results, are widely used to
facilitate fast queries on large datasets. When new records arrive at a high
rate, it is infeasible to continuously update (maintain) MVs and a common
solution is to defer maintenance by batching updates together. Between batches
the MVs become increasingly stale with incorrect, missing, and superfluous rows
leading to increasingly inaccurate query results. We propose Stale View
Cleaning (SVC) which addresses this problem from a data cleaning perspective.
In SVC, we efficiently clean a sample of rows from a stale MV, and use the
clean sample to estimate aggregate query results. While approximate, the
estimated query results reflect the most recent data. As sampling can be
sensitive to long-tailed distributions, we further explore an outlier indexing
technique to give increased accuracy when the data distributions are skewed.
SVC complements existing deferred maintenance approaches by giving accurate and
bounded query answers between maintenance. We evaluate our method on a
generated dataset from the TPC-D benchmark and a real video distribution
application. Experiments confirm our theoretical results: (1) cleaning an MV
sample is more efficient than full view maintenance, (2) the estimated results
are more accurate than using the stale MV, and (3) SVC is applicable for a wide
variety of MVs.
