Download or read book Representing Probabilistic Knowledge in Relational Databases written by International Business Machines Corporation. Research Division and published by . This book was released on 1990 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "As knowledge bases are enlarged to support more complex classes of problems, expert systems will demand efficient knowledge-management techniques -- techniques that are already available in database systems. In this paper, we present the design of a database schema suitable for [sic] knowledge base that employ [sic] a decision-network representation. Using this schema, we describe the process of translating existing knowledge bases into relational format. Although exploratory in nature, our work indicates that the application of database techniques offer numerous advantages over an ad-hoc scheme for managing probabilistic knowledge bases."
Download or read book Probabilistic Databases written by Dan Suciu and published by Morgan & Claypool Publishers. This book was released on 2011-07-07 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Probabilistic Databases written by Dan Suciu and published by Springer Nature. This book was released on 2022-05-31 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Probabilistic Ranking Techniques in Relational Databases written by Ihab Ilyas and published by Springer Nature. This book was released on 2022-05-31 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Relational Data Mining written by Saso Dzeroski and published by Springer Science & Business Media. This book was released on 2001-08 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Download or read book Probabilistic Ranking Techniques in Relational Databases written by Ihab F. Ilyas and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Query Processing on Probabilistic Data written by Guy van den Broeck and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Web Technologies Concepts Methodologies Tools and Applications written by Tatnall, Arthur and published by IGI Global. This book was released on 2009-10-31 with total page 2699 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the technological advancement of mobile devices, social networking, and electronic services, Web technologies continues to play an ever-growing part of the global way of life, incorporated into cultural, economical, and organizational levels. Web Technologies: Concepts, Methodologies, Tools, and Applications (4 Volume) provides a comprehensive depiction of current and future trends in support of the evolution of Web information systems, Web applications, and the Internet. Through coverage of the latest models, concepts, and architectures, this multiple-volume reference supplies audiences with an authoritative source of information and direction for the further development of the Internet and Web-based phenomena.
Download or read book Statistical Relational Artificial Intelligence written by Luc De Kang and published by Springer Nature. This book was released on 2022-05-31 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Download or read book Relational Data Mining written by Saso Dzeroski and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Download or read book Proceedings written by Randolph Miller and published by . This book was released on 1990 with total page 1118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Managing and Mining Uncertain Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-07-08 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Principles of Knowledge Representation and Reasoning written by A. G. Cohn and published by Morgan Kaufmann. This book was released on 2000 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Information Modelling and Knowledge Bases XXV written by IOS Press and published by IOS Press. This book was released on 2014-01-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of our ever increasing use of and reliance on technology and information systems, information modelling and knowledge bases continue to be important topics in those academic communities concerned with data handling and computer science. As the information itself becomes more complex, so do the levels of abstraction and the databases themselves. This book is part of the series Information Modelling and Knowledge Bases, which concentrates on a variety of themes in the important domains of conceptual modeling, design and specification of information systems, multimedia information modeling, multimedia systems, ontology, software engineering, knowledge and process management, knowledge bases, cross-cultural communication and context modeling. Theoretical disciplines, including cognitive science, artificial intelligence, logic, linguistics and analytical philosophy, also receive attention. The selected papers presented here cover many areas of information modeling and knowledge bases including: theory of concepts, semantic computing, data mining, context-based information retrieval, ontological technology, image databases, temporal and spatial databases, document data management, software engineering, cross-cultural computing, environmental analysis, social networks, WWW information management, and many others. This new issue also contains papers initiated by the panels on: “Cross-cultural Communication with Icons and Images” and “Conceptual Modelling of Collaboration for Information Systems”. The book will be of interest to all those interested in advances in research and applications in the academic disciplines concerned.
Download or read book Flexible Databases Supporting Imprecision and Uncertainty written by Gloria Bordogna and published by Springer. This book was released on 2007-06-02 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers the advice of selected expert contributors on the application of heterogeneous methods for managing uncertainty and imprecision in databases. It contains both survey chapters on classic topics such as "flexible querying in databases", and up to date information on "database models to represent imperfect data". Further, it includes specific contributions on uncertainty management in database integration, and in representing and querying semistructured and spatial data.
Download or read book A Probabilistic Relational Data Model written by Daniel Barbará and published by . This book was released on 1989 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "It is often desirable to represent database entities whose properties cannot be deterministically classified. We develop a new data model that includes probabilities associated with the values of the attributes. The notion of missing probabilities is introduced for partially specified probability distributions. This new model offers a richer descriptive language allowing the database to more accurately reflect the uncertain real world. Probabilistic analogs to the basic relational operators are defined and their correctness is studied."
