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Department: Computer Science and Information Systems | ||
Description: "Approximate query processing (or AQP) is an emerging research topic\ | ||
\ in big data analytics. AQP focuses on deriving fast and accurate estimations for\ | ||
\ complex queries that are usually time-consuming and expensive to run on large\ | ||
\ datasets. Traditional methods, such as histogram and sketch, are insufficient\ | ||
\ when applied to big data because of the processing limits. An essential question\ | ||
\ lacking research is how to assess the errors of AQP estimations.\nThis project\ | ||
\ focuses on assessing the errors of AQP query estimations, especially for common\ | ||
\ selection queries. Traditional methods can generate confidence intervals for query\ | ||
\ estimations based on strict assumptions such as the normal distribution assumption.\ | ||
\ Therefore, they are not applicable to massive datasets. In this project, the PI\ | ||
\ will employ a novel non-parametric statistical method called bootstrap sampling\ | ||
\ which requires less strict assumptions and brings many statistical advantages.\n\ | ||
A prototype system will be developed employing bootstrap sampling to efficiently\ | ||
\ compute standard errors and confidence intervals for AQP systems, especially those\ | ||
\ answering selection queries, namely \u03C3-AQP. Selection queries comprise a large\ | ||
\ portion of daily data queries. For broader applications, this framework will allow\ | ||
\ selection queries to include common aggregation operators such as average, sum,\ | ||
\ and count. The PI will investigate the computing and storage costs when bootstrap\ | ||
\ replicas are computed. A framework will be developed to automate both the AQP\ | ||
\ estimation and error estimation operations. Extensive benchmarks will be performed\ | ||
\ on large datasets such as the TPC-H benchmark." | ||
FieldOfScience: Computer Science | ||
FieldOfScienceID: '11.0701' | ||
InstitutionID: "https://osg-htc.org/iid/afu2chyu4qlj" | ||
Organization: Youngstown State University | ||
PIName: Feng Yu |