NDLI: Top-k queries on uncertain data: On score distribution and typical answers

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Top-k queries on uncertain data: On score distribution and typical answers

Content Provider	CiteSeerX
Author	Ge, Tingjian Zdonik, Stan Madden, Samuel
Description	Uncertain data arises in a number of domains, including data integration and sensor networks. Top-k queries that rank results according to some user-defined score are an important tool for exploring large uncertain data sets. As several recent papers have observed, the semantics of top-k queries on uncertain data can be ambiguous due to tradeoffs between reporting high-scoring tuples and tuples with a high probability of being in the resulting data set. In this paper, we demonstrate the need to present the score distribution of top-k vectors to allow the user to choose between results along this score-probability dimensions. One option would be to display the complete distribution of all potential top-k tuple vectors, but this set is too large to compute. Instead, we propose to provide a number of typical vectors that effectively sample this distribution. We propose efficient algorithms to compute these vectors. We also extend the semantics and algorithms to the scenario of score ties, which is not dealt with in the previous work in the area. Our work includes a systematic empirical study on both real dataset and synthetic datasets.
File Format	PDF
Language	English
Publisher Institution	In SIGMOD 2009
Access Restriction	Open
Subject Keyword	Typical Vector Several Recent Paper Data Set Typical Answer Real Dataset Data Integration Top-k Vector Uncertain Data Arises High Probability Potential Top-k Tuple Vector High-scoring Tuples Complete Distribution Previous Work Efficient Algorithm Important Tool Uncertain Data Large Uncertain Data Set User-defined Score Top-k Query Synthetic Datasets Systematic Empirical Study Score Distribution Sensor Network Score-probability Dimension Score Tie
Content Type	Text
Resource Type	Article

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