NDLI: Top-K oracle: A new way to present top-k tuples for uncertain data

Content Provider	IEEE Xplore Digital Library
Author	Chunyao Song Zheng Li Tingjian Ge
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Massachusetts, Lowell, MA, USA (Chunyao Song; Zheng Li; Tingjian Ge)
Abstract	Managing noisy and uncertain data is needed in a great number of modern applications. A major difficulty in managing such data is the sheer number of query result tuples with diverse probabilities. In many cases, users have a preference over the tuples in a deterministic world, determined by a scoring function. Yet it has been a challenging problem to return top-k for uncertain data. Various semantics have been proposed, and they have been shown to give wildly different tuple rankings. In this paper, we propose a completely different approach. Instead of returning users fc tuples, which are merely one point in the complex distribution of top-k tuple vectors, we provide a so-called top-k oracle and users can arbitrarily query it. Intuitively, an oracle is a black box that, whenever given an SQL query, returns its result. Any information we give is based on faithful, best-effort estimates of the ground-truth top-k tuples. This is especially critical in emergency response applications and in monitoring top-k applications. Furthermore, we are the first to provide the nested query capability with the uncertain top-k result being a subquery. We devise various query processing algorithms for top-k oracles, and verify their efficiency and accuracy through a systematic evaluation over real-world and synthetic datasets.
Sponsorship	IEEE Comput. Soc.
Starting Page	146
Ending Page	157
File Size	465019
Page Count	12
File Format	PDF
ISBN	9781467349093
ISSN	10636382
e-ISBN	9781467349109
e-ISBN	9781467349086
DOI	10.1109/ICDE.2013.6544821
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation algorithms Algorithm design and analysis Approximation methods Semantics Vectors Query processing Servers
Content Type	Text
Resource Type	Article

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