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Title: Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data
Uncertainty arises naturally in many 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 data models. 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.  more » « less
Award ID(s):
2107107 1956123 1750460 2420577 2420691
PAR ID:
10464468
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the VLDB Endowment
Volume:
16
Issue:
6
ISSN:
2150-8097
Page Range / eLocation ID:
1346 to 1358
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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