Ranking Queries On Uncertain Data
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Ranking Queries on Uncertain Data
Author: Ming Hua
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-03-28
Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data. Theoretical and algorithmic results of ranking queries on uncertain data are presented in the last section of this book. Ranking Queries on Uncertain Data is the first book to systematically discuss the problem of ranking queries on uncertain data.
Probabilistic Ranking Techniques in Relational Databases
Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion
Ranking Queries on Uncertain Data
Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-K queries) are often natural and useful in analyzing uncertain data. In this thesis, we study the problem of ranking queries on uncertain data. Specifically, we extend the basic uncertain data model in three directions, including uncertain data streams, probabilistic linkages, and probabilistic graphs, to meet various application needs. Moreover, we develop a series of novel ranking queries on uncertain data at different granularity levels, including selecting the most typical instances within an uncertain object, ranking instances and objects among a set of uncertain objects, and ranking the aggregate sets of uncertain objects. To tackle the challenges on efficiency and scalability, we develop efficient and scalable query evaluation algorithms for the proposed ranking queries. First, we integrate statistical principles and scalable computational techniques to compute exact query results. Second, we develop efficient randomized algorithms to approximate the answers to ranking queries. Third, we propose efficient approximation methods based on the distribution characteristics of query results. A comprehensive empirical study using real and synthetic data sets verifies the effectiveness of the proposed ranking queries and the efficiency of our query evaluation methods.