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Supporting User-Defined Functions on Uncertain Data

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http://www.vldb.org/pvldb/vol6/p469-tran.pdf
Original languageEnglish
Pages (from-to)469-480
Number of pages12
JournalProceedings of the VLDB Endowment (PVLDB)
Volume6
Issue number6
Publication statusPublished - 2013

Abstract

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs.

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