Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data (Extended version)

TL;DR

This paper introduces an efficient method for approximating ranking and window queries over uncertain data, supporting windowed aggregation and outperforming existing techniques in accuracy and efficiency.

Contribution

It presents the first integrated approach supporting windowed aggregation over uncertain data with efficient algorithms and implementation in PostgreSQL.

Findings

Outperforms all competitors in efficiency and accuracy.

Supports windowed aggregation over uncertain data.

Effective integration with existing uncertain data query techniques.

Abstract

Uncertainty arises naturally inmany application domains due to, e.g., data entry errors and ambiguity in data cleaning. Prior work in incomplete and probabilistic databases has investigated the semantics and efficient evaluation of ranking and top-k queries over uncertain data. However, most approaches deal with top-k and ranking in isolation and do represent uncertain input data and query results using separate, incompatible datamodels. We present an efficient approach for under- and over-approximating results of ranking, top-k, and window queries over uncertain data. Our approach integrates well with existing techniques for querying uncertain data, is efficient, and is to the best of our knowledge the first to support windowed aggregation. We design algorithms for physical operators for uncertain sorting and windowed aggregation, and implement them in PostgreSQL.We evaluated our approach on synthetic and real world datasets, demonstrating that it outperforms all competitors, and often produces more accurate results.