Query Processing On Probabilistic Data
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Query Processing On Probabilistic Data
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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 Databases
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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
Probabilistic Databases
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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
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
Query Processing On Probabilistic Data
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Author : Guy van den Broeck
language : en
Publisher:
Release Date : 2015
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 2015 with categories.
Efficient Query Processing On Probabilistic Data Treams
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Author : Maksim Goman
language : en
Publisher:
Release Date : 2017
Efficient Query Processing On Probabilistic Data Treams written by Maksim Goman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Unsicherheiten gibt es in vielen verschiedenen Daten, wie z.B. Voraussagen und Abschätzungen, Messungen, mehrdeutigen Beobachtungen, inkonsistenten Modellen und ähnlichem. Datenströme mit ungewisser Informationen wie beispielsweise schwankende Genauigkeit, ungenaue Daten, Rauschen, Fehler und Unabgeschlossenheit finden mehr und mehr Aufmerksamkeit. Für fehlerhafte Streams können die Methoden für deterministische Datenströmen nicht verwendet werden. Es gibt bereits viel Forschung im Bereich von unsicheren Datenbanken um das Problem zu lösen. Unsichere Datenströme stellen eine größere Herausforderung dar, da mit echtzeitfähigen Modellen gearbeitet wird, welche unsicheren Daten über stark schwankende Datenübertragungen abfragen oder um Sequenzbearbeitung in Datenströmen durchführen. Die tatsächliche Verteilung von Attributen aus gegebenen bedingten Attributen und die Existenz von unsicheren Tupel sind ein großes Problem. Die Algorithmen und die Implementierung muss effizient genug sein um den Standard der schnellen Verarbeitung von Datenströmen gerecht zu werden. Wir berücksichtigen, in dieser Arbeit, Abfragen für Bedingungen (SELECT und GROUP BY) sowie Aggregationsoperatoren (z.B. SUM) für unsichere Datenströme. Das Ziel dieser Arbeit ist eine effiziente Beurteilung und verwendet ein unsicheres Datenmodell für schnelle Anfragenbearbeitung. Um das Ziel zu erreichen wird ein angemessenes Verhältnis von Präzession und hoher Bearbeitungsgeschwindigkeit gewählt. Hierfür wird ein schneller Datenstromalgorithmus designt. Anstatt für jeden Mittelwert die gesamten Wahrscheinlichkeiten zu errechnen, wird ein Satz von sechs Parametern verwendet, der wichtige Quantile enthält. Das ermöglicht schnellere Berechnungen für die Strombearbeitung. Wir verwenden das einfache und transparente Abfrage-Modell, auf der Basis von der wahrscheinlichkeitszwang Methode. Dies ermöglicht eine einfache Semantik für unsichere Abfragen, die mathematisch belegt sind. Unsichere Operatoren als Optimierungsproblem zu beschreiben, kann für weitere Abfragenverarbeitung hilfreich sein. Experimente zeigen eine gute Leistung von unsicherer Aggregation und ist dadurch für zukünftige weitere Forschungsrichtungen hilfreich.
Extracting And Querying Probabilistic Information In Bayesstore
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Author : Zhe Wang
language : en
Publisher:
Release Date : 2011
Extracting And Querying Probabilistic Information In Bayesstore written by Zhe Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
During the past few years, the number of applications that need to process large-scale data has grown remarkably. The data driving these applications are often uncertain, as is the analysis, which often involves probabilistic models and statistical inference. Examples include sensor-based monitoring, information extraction, and online advertising. Such applications require probabilistic data analysis (PDA), which is a family of queries over data, uncertainties, and probabilistic models that involve relational operators from database literature, as well as inference operators from statistical machine learning (SML) literature. Prior to our work, probabilistic database research advocated an approach in which uncertainty is modeled by attaching probabilities to data items. However, such systems do not and cannot take advantage of the wealth of SML research, because they are unable to represent and reason the pervasive probabilistic correlations in the data. In this thesis, we propose, build, and evaluate BayesStore, a probabilistic database system that natively supports SML models and various inference algorithms to perform advanced data analysis. This marriage of database and SML technologies creates a declarative and efficient probabilistic processing framework for applications dealing with large-scale uncertain data. We use sensor-based monitoring and information extraction over text as the two driving applications. Sensor network applications generate noisy sensor readings, on top of which a first-order Bayesian network model is used to capture the probability distribution. Information extraction applications generate uncertain entities from text using linear-chain conditional random fields. We explore a variety of research challenges, including extending the relational data model with probabilistic data and statistical models, efficiently implementing statistical inference algorithms in a database, defining relational operators (e.g., select, project, join) over probabilistic data and models, developing joint optimization of inference operators and the relational algebra, and devising novel query execution plans. The experimental results show: (1) statistical inference algorithms over probabilistic models can be efficiently implemented in the set-oriented programming framework in databases; (2) optimizations for query-driven SML inference lead to orders-of-magnitude speed-up on large corpora; and (3) using in-database SML methods to extract and query probabilistic information can significantly improve answer quality.
Advances In Probabilistic Databases For Uncertain Information Management
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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.
Probabilistic Methods In Query Processing
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Author : S. Seshadri
language : en
Publisher:
Release Date : 1992
Probabilistic Methods In Query Processing written by S. Seshadri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
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