Ranking Queries On Uncertain Data
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Ranking Queries On Uncertain Data
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Author : Ming Hua
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28
Ranking Queries On Uncertain Data written by Ming Hua and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.
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.
Ranking Queries On Uncertain Data
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Author : Ming Hua
language : en
Publisher:
Release Date : 2009
Ranking Queries On Uncertain Data written by Ming Hua and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Data mining categories.
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.
Ranked Retrieval In Uncertain And Probabilistic Databases
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Author : Mohamed A. Soliman
language : en
Publisher:
Release Date : 2010
Ranked Retrieval In Uncertain And Probabilistic Databases written by Mohamed A. Soliman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
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 dissertation introduces 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 studying 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 introduce a processing framework leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. The framework encapsulates a state space model, and efficient search algorithms that compute query answers by lazily materializing the necessary parts of the space. Under the attribute-level uncertainty model, we give a new probabilistic ranking model, based on partial orders, to encapsulate the space of possible rankings originating from uncertainty in attribute values. We present a set of efficient query evaluation algorithms, including sampling-based techniques based on the theory of Markov chains and Monte-Carlo method, to compute query answers. We build on our techniques for ranking under attribute-level uncertainty to support rank join queries on uncertain data. We show how to extend current rank join methods to handle uncertainty in scoring attributes. We provide a pipelined query operator implementation of uncertainty-aware rank join algorithm integrated with sampling techniques to compute query answers.
Probabilistic Ranking Techniques In Relational Databases
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Author : Ihab Ilyas
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Probabilistic Ranking Techniques In Relational Databases written by Ihab Ilyas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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
Probabilistic Ranking Techniques In Relational Databases
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Author : Ihab F. Ilyas
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011
Probabilistic Ranking Techniques In Relational Databases written by Ihab F. Ilyas and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
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
Query Processing Over Uncertain Databases
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Author : Lei Chen
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2012-12-01
Query Processing Over Uncertain Databases written by Lei Chen and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-01 with Computers categories.
Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion
Querying And Mining Uncertain Data Streams
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Author : Cheqing Jin
language : en
Publisher: World Scientific
Release Date : 2016-05-24
Querying And Mining Uncertain Data Streams written by Cheqing Jin and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-24 with Computers categories.
Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space.This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering.Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields.
Rough Sets Fuzzy Sets Data Mining And Granular Computing
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Author : Davide Ciucci
language : en
Publisher: Springer
Release Date : 2013-10-07
Rough Sets Fuzzy Sets Data Mining And Granular Computing written by Davide Ciucci and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-07 with Computers categories.
This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of the co-located conference of the 2013 Joint Rough Set Symposium, JRS 2013. The 69 papers (including 44 regular and 25 short papers) included in the JRS proceedings (LNCS 8170 and LNCS 8171) were carefully reviewed and selected from 106 submissions. The papers in this volume cover topics such as inconsistency, incompleteness, non-determinism; fuzzy and rough hybridization; granular computing and covering-based rough sets; soft clustering; image and medical data analysis.
Managing And Mining Uncertain Data
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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-08
Managing And Mining Uncertain Data written by Charu C. Aggarwal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-08 with Computers categories.
Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Transactions On Large Scale Data And Knowledge Centered Systems Xliii
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Author : Abdelkader Hameurlain
language : en
Publisher: Springer Nature
Release Date : 2020-08-12
Transactions On Large Scale Data And Knowledge Centered Systems Xliii written by Abdelkader Hameurlain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-12 with Computers categories.
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 43rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include classification tasks, machine learning algorithms, top-k queries, business process redesign and a knowledge capitalization framework.