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Query Processing
ZIP: Lazy Imputation during Query Processing
Yiming Lin
,
Sharad Mehrotra
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Auto-BI
Auto-BI helps end-users by accurately predicting BI models given a set of input tables by developing a principled graph-based optimization problem in Auto-BI that considers both local join prediction and global schema-graph structures. Extensive experiments on 1000 real test cases suggest that Auto-BI is both efficient and accurate, achieving over 90% F1-score when evaluated against ground-truth BI models that humans design.
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ZIP
ZIP develops a query-time missing value imputation framework that minimizes the joint costs of imputation and query execution. QUIP outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets, and achieves the order-of-magnitudes improvement over offline approach.
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EnrichDB
EnrichDB is a system designed to support just-in-time data enrichment during query processing. EnrichDB is motivated by applications that consume (potentially large volumes of) raw data that must first be interpreted using expensive machine learning / signal processing functions prior to being queried/used in analysis. Executing such enrichment during data ingestion (to support real-time analytics) is challenging to scale specially when dataset can be very large and/or when data arrives at a high velocity. EnrichDB addresses this challenge by supporting enrichment at all phases of data processing including intermixing enrichment with query processing. It exploits query context to steer enrichment in ways such that the query results can be computed progressively. EnrichDB is implemented using a layered approach on top of PostgreSQL, though it can easily be layered on other databases.
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