Probabilistic Databases
DOWNLOAD
Download Probabilistic Databases PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Probabilistic Databases book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Probabilistic Databases
DOWNLOAD
Author : Dan Suciu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Probabilistic Databases written by Dan Suciu 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.
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques
Probabilistic Databases
DOWNLOAD
Author : Dan Suciu
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011-07-07
Probabilistic Databases written by Dan Suciu 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 2011-07-07 with Technology & Engineering categories.
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques
Advances In Probabilistic Databases For Uncertain Information Management
DOWNLOAD
Author : Zongmin Ma
language : en
Publisher: Springer
Release Date : 2013-03-30
Advances In Probabilistic Databases For Uncertain Information Management written by Zongmin Ma and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-30 with Technology & Engineering categories.
This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
Query Processing On Probabilistic Data
DOWNLOAD
Author : Guy Van den Broeck
language : en
Publisher:
Release Date : 2017
Query Processing On Probabilistic Data written by Guy Van den Broeck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic books categories.
Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the AI community have studied various aspects of probabilistic relational data. This survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. The survey starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. After that, the survey discusses lifted probabilistic inference, which are a suite of techniques developed in parallel by the Database and AI communities for probabilistic query evaluation. Then, it gives a short summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. The survey ends with a very brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.
Probabilistic Ranking Techniques In Relational Databases
DOWNLOAD
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
Managing Query Quality In Probabilistic Databases
DOWNLOAD
Author : Xiang Li
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-26
Managing Query Quality In Probabilistic Databases written by Xiang Li and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.
This dissertation, "Managing Query Quality in Probabilistic Databases" by Xiang, Li, 李想, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In many emerging applications, such as sensor networks, location-based services, and data integration, the database is inherently uncertain. To handle a large amount of uncertain data, probabilistic databases have been recently proposed, where probabilistic queries are enabled to provide answers with statistical guarantees. In this thesis, we study the important issues of managing the quality of a probabilistic database. We first address the problem of measuring the ambiguity, or quality, of a probabilistic query. This is accomplished by computing the PWS-quality score, a recently proposed measure for quantifying the ambiguity of query answers under the possible world semantics. We study the computation of the PWS-quality for the top-k query. This problem is not trivial, since directly computing the top-k query score is computationally expensive. To tackle this challenge, we propose efficient approximate algorithms for deriving the quality score of a top-k query. We have performed experiments on both synthetic and real data to validate their performance and accuracy. Our second contribution is to study how to use the PWS-quality score to coordinate the process of cleaning uncertain data. Removing ambiguous data from a probabilistic database can often give us a higher-quality query result. However, this operation requires some external knowledge (e.g., an updated value from a sensor source), and is thus not without cost. It is important to choose the correct object to clean, in order to (1) achieve a high quality gain, and (2) incur a low cleaning cost. In this thesis, we examine different cleaning methods for a probabilistic top-k query. We also study an interesting problem where different query users have their own budgets available for cleaning. We demonstrate how an optimal solution, in terms of the lowest cleaning costs, can be achieved, for probabilistic range and maximum queries. An extensive evaluation reveals that these solutions are highly efficient and accurate. DOI: 10.5353/th_b4775313 Subjects: Query languages (Computer science) Databases Probabilistic number theory
Probabilistic Ranking Techniques In Relational Databases
DOWNLOAD
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
Scalable Query Evaluation Over Complex Probabilistic Databases
DOWNLOAD
Author : Abhay Jha
language : en
Publisher:
Release Date : 2012
Scalable Query Evaluation Over Complex Probabilistic Databases written by Abhay Jha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Probabilistic databases categories.
The age of Big Data has brought with itself datasets which are not just big, but also much more complicated. These datasets are constructed from disparate, unreliable and noisy sources, many times in an ad-hoc way because careful data cleaning and integration is too time consuming and not always necessary anymore. Representing the uncertainty hidden in these datasets is necessary to get meaningful query answers and Probabilistic Databases have come up as arguably the most popular solution to this problem. Their application to practical problems though has been held back because (i) the common models they use are not rich enough to capture the dependencies in these problems, and (ii) unlike traditional databases, query evaluation for probabilistic databases can be very expensive and unpredictable. This dissertation addresses these challenges by first proposing a new model for probabilistic databases that is rich enough to capture the dependencies found in most practical applications, while still allowing for a translation to considerably simpler and well-studied models. Our model leverages existing models from AI literature that combine probability theory with logic. The main challenge of query evaluation over probabilistic databases is that it requires solving probabilistic inference which is a notoriously hard problem. This dissertation studies this problem via both (i) foundational results that give new theoretical insights about existing probabilistic inference algorithms, like Read-Once Formulas, Tree-Decompositions, Binary Decision Diagrams, Negation Normal Forms, when applied to the setting of probabilistic databases, which as we will see have their own distinct challenges and expectations, and (ii) building a robust system where the above ideas are leveraged for efficient and reliable query evaluation.
Managing And Mining Uncertain Data
DOWNLOAD
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.
Learning Tuple Probabilities In Probabilistic Databases
DOWNLOAD
Author : Maximilian Dylla
language : en
Publisher:
Release Date : 2014
Learning Tuple Probabilities In Probabilistic Databases written by Maximilian Dylla and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.