Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Beten batches the MVs become increasingly stale with incorrect, missing, and superfluous rows leading to increasingly inaccurate query results.  propose Stale View Cleaning (SVC) which addresses this problem from a data cleaning perspective. In SVC,  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,  further explore an outlier indexing technique to give increased accuracy when the data distributions are sked. SVC complements existing deferred maintenance approaches by giving accurate and bounded query ansrs beten maintenance.  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.
['Sanjay Krishnan', 'Jiannan Wang', 'Michael J. Franklin', 'Ken Goldberg', 'Tim Kraska']
I am writing a research paper on a idea Stale view cleaning: getting fresh answers from stale materialized views
[{"from": "human", "value": "I am writing a research paper on a idea Stale view cleaning: getting fresh answers from stale materialized views"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Beten batches the MVs become increasingly stale with incorrect, missing, and superfluous rows leading to increasingly inaccurate query results.  propose Stale View Cleaning (SVC) which addresses this problem from a data cleaning perspective. In SVC,  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,  further explore an outlier indexing technique to give increased accuracy when the data distributions are sked. SVC complements existing deferred maintenance approaches by giving accurate and bounded query ansrs beten maintenance.  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."}]