Query Processing

Predicate pushing down is a key optimization used to speed up query processing. Much of the existing practice is restricted to pushing predicates explicitly listed in the query. In this paper, we consider the challenge of learning predicates during query execution which are then exploited to accelerate execution. Prior related approaches with a similar goal are restricted (e.g., learn from only join columns or from specific data statistics). We significantly expand the realm of predicates that can be learned from different query operators (aggregations, joins, grouping, etc.) and develop a system, entitled PLAQUE, that learns such predicates during query execution. Comprehensive evaluations on both synthetic and real datasets demonstrate that the learned predicate approach adopted by PLAQUE can significantly accelerate query execution by up to 33x, and this improvement increases to up to 100x when User-Defined Functions (UDFs) are utilized in queries.

This project develops a query-time missing value imputation framework, entitled ZIP, that modifies relational operators to be imputation-aware in order to minimize the joint cost of imputing and query processing. The modified operators use a cost-based decision function to determine whether to invoke imputation or to defer to downstream operators to resolve missing values. The modified query processing logic ensures results with deferred imputations are identical to those produced if all missing values were imputed first. ZIP includes a novel outer-join based approach to preserve missing values during execution, and a bloom filter based index to optimize the space and running overhead. Extensive experiments on both real and synthetic data sets demonstrate 10 to 25 times improvement when augmenting the state-of-the-art technology, ImputeDB, with ZIP-based deferred imputation. ZIP also outperforms the offline approach by up to 19607 times in a real data set.

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.