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Materialized views (MVs), stored pre-computed results, are widely used tofacilitate fast queries on large datasets. When new records arrive at a highrate, it is infeasible to continuously update (maintain) MVs and a commonsolution is to defer maintenance by batching updates together. Between batchesthe MVs become increasingly stale with incorrect, missing, and superfluous rowsleading to increasingly inaccurate query results. We propose Stale ViewCleaning (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 theclean sample to estimate aggregate query results. While approximate, theestimated query results reflect the most recent data. As sampling can besensitive to long-tailed distributions, we further explore an outlier indexingtechnique to give increased accuracy when the data distributions are skewed.SVC complements existing deferred maintenance approaches by giving accurate andbounded query answers between maintenance. We evaluate our method on agenerated dataset from the TPC-D benchmark and a real video distributionapplication. Experiments confirm our theoretical results: (1) cleaning an MVsample is more efficient than full view maintenance, (2) the estimated resultsare more accurate than using the stale MV, and (3) SVC is applicable for a widevariety of MVs.

Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views